AN ABSTRACT OF THE DISSERTATION OF
Man Li for the degree of Doctor of Philosophy in Agricultural and Resource Economics
presented on June 16, 2010.
Title: Essays on Land-use Change, Carbon Sequestration and Emissions in China.
Abstract approved:
JunJie Wu
Jeffrey J. Reimer
China has experienced rapid economic growth in the last twenty years,
accompanied by large-scale land conversion, severe environmental degradation, and
rising carbon dioxide (CO2) emissions. Designing policies for sustainable development
requires a comprehensive understanding of the relationship between economic growth,
land-use change, carbon sequestration and emissions in China. This dissertation consists
of three essays that address several relevant issues from an economic perspective.
The first essay presents an empirical analysis to identify the major drivers of
land-use change in China for the period 1988-2000 by using highly-disaggregated,
national-scale GIS land use data and a state-of-art econometric method. Results indicate
that GDP growth and agricultural investment had relatively larger impacts on farmland
conversion, while population growth and agricultural investment were more influential in
grassland loss. Implications of the results for the design of farmland protection policies
are discussed.
The second essay examines the relationship between land-use change and soil
carbon sequestration in China. Results indicate that farmland and grassland loss,
deforestation, and land idleness, driven by GDP growth, accelerated soil carbon runoff.
Implementation of the green growth policy could generate up to 0.7-1.1 million Mg SOC
and result in 22.2-37.4 million CNY welfare losses annually from 2001 to 2050. The
marginal welfare loss is approximately ¥15.3/Mg (equivalent to $2.25/Mg) for
sequestering about 1 million Mg SOC per year.
The third essay presents a new method to examine the sources of change in CO2
emissions in China between 1991 and 2006. Results indicate that GDP scale effect
accounted for the majority of emission increments. The emission index associated with
capital was a dominant contributor to emission abatement. The effects of technical
change in production and change in the GDP-composition by sector played positive roles
in curtailing emissions.
©Copyright by Man Li
June 16, 2010
All Right Reserved
ESSAYS ON LAND-USE CHANGE, CARBON SEQUESTRATION AND EMISSIONS IN CHINA
by
MAN LI
A DISSERTATION
submitted to
Oregon State University
in partial fulfillment of
the requirements of the
degree of
DOCTOR OF PHILOSOPHY
Presented June 16, 2010
Commencement June 2011
Doctor of Philosophy dissertation of Man Li presented on June 16, 2010.
APPROVED:
Co-Major Professor, representing Agricultural and Resource Economics
Co-Major Professor, representing Agricultural and Resource Economics
Head of the Department of Agricultural and Resource Economics
Dean of the Graduate School
I understand that my dissertation will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my dissertation to
any reader upon request.
Man Li, Author
AKNOWLEDGEMENTS
I have been fortunate to have a number of excellent people who have assisted my
degree and without whom this dissertation would have not been possible. I wish to
express sincere gratitude to all members of my PhD program committee, Dr. JunJie Wu,
Dr. Jeffrey J. Reimer, Dr. Andrew J. Plantinga, Dr. Lan Xue, and Dr. Russell E. Ingham,
for their invaluable comments and suggestions on this dissertation. I particularly
appreciate the consistent support, guidance, and encouragement of my co-major professor,
Dr. JunJie Wu. All of his comments and suggestions have left mark on this work. A
special thanks is extended to Dr. Xiangzheng Deng, who has been helpful in collecting
most data used in this dissertation.
I would also like to thank my family for their patient support and encouragement
throughout these years.
CONTRIBUTION OF AUTHORS
Dr. JunJie Wu was involved in the design, analysis, and writing of Chapters 2-3.
Dr. Xiangzheng Deng assisted with data collection of Chapters 2-3.
TABLE OF CONTENTS
Page
1.
Introduction ...................................................................................................................1
References ................................................................................................................6
2.
Indentifying Drivers of Land-use Change in China: A Spatial Multinomial Logit
Model Analysis .............................................................................................................7
Abstract ....................................................................................................................8
Introduction ..............................................................................................................9
The Model ..............................................................................................................13
Empirical Specification .....................................................................................13
Econometric Issues ............................................................................................15
Data ........................................................................................................................19
results .....................................................................................................................23
Drivers of Land-use Changes .................................................................................35
Public Agricultural Investment and Farmland Protection ......................................38
Conclusions ............................................................................................................41
Endnotes .................................................................................................................43
Acknowledgements ................................................................................................45
References ..............................................................................................................46
Appendices .............................................................................................................49
3.
An Empirical Economic Analysis of Land-use Change and Soil Carbon
Sequestration in China ................................................................................................64
Abstract ..................................................................................................................65
TABLE OF CONTENTS (CONTINUED)
Page
Introduction ............................................................................................................66
The SOC Density Model ........................................................................................69
Data ........................................................................................................................73
results .....................................................................................................................75
SOC Density Model ..........................................................................................75
Land Use Change Model ...................................................................................77
Green Growth, Carbon Sequestration, and Welfare Loss ......................................79
Conclusions ............................................................................................................88
Endnotes .................................................................................................................90
Acknowledgements ................................................................................................91
References ..............................................................................................................92
4.
Decomposing the Change of CO2 Emissions in China: A Distance Function
Approach .....................................................................................................................96
Abstract ..................................................................................................................97
Introduction ............................................................................................................98
Methodology ........................................................................................................100
Application ...........................................................................................................106
Data .................................................................................................................106
Result and Discussions ....................................................................................107
Concluding Comments .........................................................................................113
Endnotes ...............................................................................................................114
TABLE OF CONTENTS (CONTINUED)
Page
Acknowledgements ..............................................................................................115
References ............................................................................................................116
Appendices ..........................................................................................................118
5.
Conclusions...............................................................................................................123
Bibliography .............................................................................................................126
LIST OF FIGURES
Figure
Page
2.1. Distribution of Land-Use Change on Farmland, 1988–2000......................................10
2.2. Distribution of Land-Use Change on Grassland, 1988–2000 .....................................11
2.3. Marginal Costs of Preserving Farmland in Counties with Different Percent Farmland
Coverage (Study Area: Huang-Huai-Hai Plain, Yangtze River Delta, and Sichuan
Basin) ..........................................................................................................................39
3.1. Histogram of R-square for all GWR‟s in the SOC density model ..............................75
3.2. Box plot of land-use dummy estimators for all GWR‟s in the SOC density model ...76
3.3. The area of land by use in the baseline scenario .........................................................81
3.4. The area of land by use in the scenario of initial 10% GDP growth rate ...................82
3.5. The flow of soil organic carbon relative to the baseline for different GDP growth
rates .............................................................................................................................83
3.6. Time series plot of expected logsum welfare gains for different GDP growth rates
relative to the baseline ................................................................................................85
3.7. The marginal welfare losses of soil carbon sequestration by discount rate under the
Green Growth policy scenario ....................................................................................86
3.8. The average welfare losses of soil carbon sequestration by discount rate under the
Green Growth policy scenario ....................................................................................87
4.1. National Carbon Dioxide Emissions in China, 1991-2006 .......................................108
LIST OF TABLES
Table
Page
2.1. Summary Statistics of Explanatory Variables ...........................................................19
2.2a. Land-use Transitions from 1988 to 1995 ..................................................................20
2.2b. Land-use Transitions from 1995 to 2000 ..................................................................20
2.3a. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Farmland, 1988-1995................................................................................25
2.3b. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Farmland, 1995-2000................................................................................26
2.4a. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Farmland, 1988-1995 .............................................................................................27
2.4b. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Farmland, 1995-2000 .............................................................................................28
2.5a. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Grassland, 1988-1995 ...............................................................................31
2.5b. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Grassland, 1995-2000 ...............................................................................32
2.6a. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Grassland, 1988-1995 ............................................................................................33
2.6b. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Grassland, 1995-2000 ............................................................................................34
2.7a. Description of Simulation Scenarios ........................................................................35
2.7b. Simulated Changes in Land Supplies of Six Major Uses, 1988-2000 ......................37
3.1. Summary Statistics of Explanatory Variables ...........................................................73
3.2. Description of Scenarios with Different Annual GDP Growth Rates .......................80
4.1. Change in CO2 Emissions and its Seven Decomposing Indices, 1991-2006 ...........109
LIST OF TABLES (CONTINUED)
Table
Page
4.2. Geometric Means of Annual Changes for Each Consecutive Two-year Period, 19912006 ..........................................................................................................................111
LIST OF APPENDIX TABLES
Table
Page
A2.1. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Forestland, 1988-1995 ..............................................................................52
A2.2. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Forestland, 1995-2000 ..............................................................................53
A2.3. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Forestland, 1988-1995 ...........................................................................................54
A2.4. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Forestland, 1995-2000 ...........................................................................................55
A2.5. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Water Area, 1988-1995 ............................................................................56
A2.6. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Water Area, 1995-2000 ............................................................................57
A2.7. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Water Area, 1988-1995..........................................................................................58
A2.8. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use Change
on Water Area, 1995-2000..........................................................................................59
A2.9. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Unused Land, 1988-1995 .........................................................................60
A2.10. Coefficient Estimates for the Standard Multinomial Logit Model of Land-use
Change on Unused Land, 1995-2000 .........................................................................61
A2.11. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use
Change on Unused Land, 1988-1995 .........................................................................62
A2.12. Coefficient Estimates for the Spatial Multinomial Logit Model of Land-use
Change on Unused Land, 1995-2000 .........................................................................63
ESSAYS ON LAND-USE CHANGE, CARBON SEQUESTRATION
AND EMISSIONS IN CHINA
CHAPTER 1
INTRODUCTION
MAN LI
2
China has been at the forefront of the surge in global economic growth, with an
approximate annual 10% increase in the gross domestic product (GDP) for the last twenty
years (NBSC 2005; 2006-07). This rapid growth has been heavily affected by policy. For
example, the growth rate reached its first peak in 1992 as Chinese reformer leader,
Xiaoping Deng, made his famous southern tour of China using his travels as a method to
reassert his economic policy, which was a milestone in the process of China‟s economic
reform. In 1998, the Chinese government implemented industrial restructuring and shut
down thousands of inefficient enterprises, including small-scale mines and power plants.
Economic growth slowed somewhat during this period, but the economy kept growing
strongly after China‟s join the World Trade Organization in 2001.
Along with this economic success, CO2 emissions have been increasing rapidly
in China since the 1990s. The emissions peaked for the first time in 1996 then dropped
off and touched the bottom in 1998, which was concurrent with the industrial
restructuring as just discussed. Again, after the entry to the WTO in 2001, the emissions
went up at a high speed of 12% per year. In particular, China surpassed the United States
to become the largest CO2 emitter of the world in 2006, releasing nearly 1.7 million
petagrams of carbon (Pg C) into the atmosphere, which is 1.4 times more than the amount
it emitted in 1991 (Marland et al. 2009).
China has also experienced rapid urbanization in the last twenty years. From
1988 to 2000, the total developed area increased by 1.78 million hectares. The fraction of
population residing in urban areas increased from 26% in 1990 to 46% in 2008 (Chinese
Academy of Social Sciences 2009). The rapid urbanization has led to dramatic land-use
changes in many parts of China, particularly in coastal regions and areas near major cities.
For example, in traditional agricultural regions such as the Huang-Huai-Hai Plain,
Yangtze River Delta and Sichuan Basin, rapid urban expansion to high-quality cultivated
land is threatening the national food security, which is an extremely important issue in
China where one-fifth of the world's population lives. The Chinese government has
implemented the Basic Farmland Protection Regulation (1994) and revised the Land
3
Administration Law (1998) to slow this trend, but these laws and regulations have only
achieved limited success.
Despite the great loss of high-quality farmland in traditional agricultural regions,
the total farmland in China increased by 2.8 million hectares from 1988 to 2000, due
largely to farmland gained through grassland conversion and deforestation. Most
grassland conversion to farmland occurred in the farming-pasture zones of East Inner
Mongolia, North China Plain, and Loess plateau. Total grassland and forestland declined
by 3.5 and 1.2 million hectares in China during this period, which reduced the soil carbon
density and made China‟s ecological environment worse off. For example, agricultural
development and desert expansion reduced the soil organic carbon (SOC) density by 1020 kg/m2 in the alpine meadow of the Southeast Tibet Plateau and by 2-3 kg/m2 in the
grassland of the Mid-east Inner Mongolia (Wang et al. 2003).
The connections between economic growth, CO2 emissions, land-use change,
and carbon sequestration are complex. Designing policies for sustainable development
requires a comprehensive understanding of these relationships. This dissertation consists
of three essays that address several relevant issues from an economic perspective.
The first essay (Chapter 2), Identifying Drivers of Land-use Change in China: A
Spatial Multinomial Logit Model Analysis, presents an empirical analysis to identify the
major drivers of land use change in China for the period 1988-2000. The analysis
compiles highly disaggregated, national-scale GIS land-use data and develops a state-ofart method to estimate a spatial multinomial logit model that takes into account of spatial
autocorrelation explicitly. Results indicate that both socioeconomic and geophysical
variables affected land-use change in China. GDP growth and agricultural investment had
larger impacts on farmland conversion, while population increase and agricultural
investment were more influential in grassland loss. The model is used to evaluate the
effectiveness of public agricultural investment as a policy instrument for farmland
protection in traditional agricultural regions (Huang-Huai-Hai Plain, Yangtze River Delta,
and Sichuan Basin). The results indicate targeting public agricultural investment in
4
counties with relatively low coverage of farmland (< 50%) is more cost-effective than
spending money across regions.
The second essay (Chapter 3), An Empirical Economic Analysis of Land-use
Change and Soil Carbon Sequestration in China, examines the relationship between
land-use change and soil carbon sequestration in China. A statistical Soil Organic Carbon
(SOC) density model and an econometric land-use change model (i.e., a version of the
multinomial logit model in the first essay) are developed to link the socioeconomic
factors with the SOC density. The approach captures spatial autocorrelation and spatial
heterogeneity simultaneously and can be applied to a large region. Results indicate that
SOC density is generally highest in forest and grass lands, and lowest in unused land in
China. GDP growth leads to farmland and grassland loss, deforestation, and idleness,
which accelerates soil carbon runoff. The models are integrated to evaluate the welfare
effects of China‟s green growth policy. The Chinese government has in 2006 announced
six major measures, including legislation, industrial structure, technology, energy
consumption management, incentive policies, and mechanism, in pursuit of green growth
and a resources saving society in conjunction with soil carbon sequestration. The
government listed resource reservation and environment protection as a major national
policy in its “11th Five-Year Plan (2006 to 2010) for National Economic and Social
Development”. The policy could generate up to 0.7-1.1 million Mg SOC and 22.2-37.4
million CNY welfare losses annually from 2001 to 2050. The marginal welfare loss curve
is approximately ¥15.3/Mg (equivalent to $2.25/Mg) to sequester about 1 million Mg
SOC per year.
The third essay (Chapter 4), Decomposing the Change of CO2 Emissions in
China: A Distance Function Approach, examines the sources of change in CO2 emissions
in China between 1991 and 2006. It evaluates the relative contributions of the sources to
emission abatement using a new empirical approach. The method uses the data
envelopment analysis (DEA) technique to decompose emission changes into seven
components based on the Shephard output distance function. The method accounts for
factors that increase carbon emissions, as well as decrease them. It allows for cross-
5
sectional analysis under flexible data requirement. Results indicate that GDP scale effect
(i.e., an expansion of the economy equivalent to the ratio of GDP between the two time
periods) accounted for the majority of emission increments. The emission index
associated with capital was a dominant contributor to emission abatement. The effects of
technical change in production and change in the GDP-composition by sector played
positive roles in reducing emissions.
6
REFERENCES
Chinese Academy of Social Sciences. 2009. “中国城镇人口 2008 年末 6.07 亿.”
http://www.cpirc.org.cn/news/rkxw_gn_detail.asp?id=10684 (accessed December
1, 2009).
Marland, G., Boden, T., Andres, R.J., 2009. National CO2 emissions from fossil-fuel
burning, cement manufacture, and gas flaring: 1751-2006, in: Trends online: A
compendium of data on global change. Carbon Dioxide Information Analysis
Center, O.R.N.L., U.S. Department of Energy, Oak Ridge Tennessee.
National Bureau of Statistics of China, 2006-07. China Statistical Yearbook. China
Statistical Press, Beijing.
National Bureau of Statistics of China, 2005. Comprehensive Statistical Data and
Materials on 55 Years of New China. China Statistical Press, Beijing.
Wang, S., Tian, H., Liu, J., Pan, S., 2003. Pattern and change of soil organic carbon
storage in China: 1960s-1980s. Tellus 55B, 416-427.
7
CHAPTER 2
INDENTIFYING DRIVERS OF LAND-USE CHANGE IN CHINA: A SPATIAL MULTINOMIAL
LOGIT MODEL ANALYSIS
MAN LI
8
ABSTRACT
This essay presents an empirical analysis to identify the major drivers of land
use change in China for the period 1988-2000. The analysis compiles highly
disaggregated, national-scale land-use data and develops a state-of-art method to estimate
a spatial multinomial logit model that takes into account of spatial autocorrelation
explicitly. Results indicate that both socioeconomic and geophysical variables affected
land-use change in China. GDP growth and agricultural investment had larger impacts on
farmland conversion, while population increase and agricultural investment were more
influential in grassland loss. The model is used to evaluate the effectiveness of public
agricultural investment as a policy tool for farmland protection in traditional agricultural
regions, including the Huang-Huai-Hai Plain, Yangtze River Delta, and Sichuan Basin.
The results indicate targeting public agricultural investment in counties with relatively
low coverage of farmland (< 50%) is more cost-effective than spending money across
regions.
9
INTRODUCTION
China has experienced rapid urbanization in the last twenty years. From 1988 to
2000, the total developed area increased by 1.78 million hectares. The fraction of
population residing in urban areas increased from 26% in 1990 to 46% in 2008 (Chinese
Academy of Social Sciences 2009). The rapid urbanization has led to dramatic land-use
changes in many parts of China, particularly in coastal regions and areas near major cities.
For example, in traditional agricultural regions such as the Huang-Huai-Hai Plain,
Yangtze River Delta and Sichuan Basin, rapid urban expansion to high-quality cultivated
land is threatening the national food security, which is an extremely important issue in
China where one-fifth of the world's population lives. The Chinese government has
implemented the Basic Farmland Protection Regulation (1994) and revised the Land
Administration Law (1998)1 to slow this trend, but these laws and regulations have only
achieved limited success. Despite the great loss of high-quality farmland in traditional
agricultural regions, the total farmland in China increased by 2.8 million hectares from
1988 to 2000, due largely to farmland gained through grassland conversion and
deforestation. Most grassland conversion to farmland occurred in the farming-pasture
zones of East Inner Mongolia, North China Plain, and Loess plateau. Total grassland and
forestland declined by 3.5 and 1.2 million hectares in China during this period, which
made China‟s ecological environment worse off.
Understanding the drivers of land-use change in China is useful for designing
efficient agricultural, environmental, land use policies. Many previous studies have made
efforts to obtain such knowledge: some of them focused on relatively small geographic
areas (Deng et al. 2002; Deng et al. 2008b; Long et al. 2007; Long et al. 2008; Ostwald
and Chen 2006), others concerned land development to urban built-up use (Deng et al.
2008a; Seto and Kaufmann 2003). Few have conducted a comprehensive, systematic
analysis of land-use change at the national scale. One exception is Liu et al. (2003), who
documented the spatial pattern of land-use change in China from 1995 to 2000, but did
not quantify the major drivers of land-use change. A majority of finding from these
10
previous studies is that both geophysical and socioeconomic factors may affect land-use
in China.
Figure 2.1. Distribution of Land-use Change on Farmland, 1988–2000.
The purposes of this essay are 1) to conduct a national-scale analysis to identify
the major drivers of land use conversions in China; 2) to assess the relative importance of
socioeconomic drivers; and 3) to design a cost-effective scheme to preserve farmland
from development in traditional agricultural regions. To achieve these objectives, we
compile a unique dataset that covers Mainland China, and develop a standard and a
spatial multinomial logit models to analyze land use choice among six major uses (i.e.,
farmland, forestland, grassland, water area, urban area, and unused land). Other data
provided by the Chinese Academy of Sciences, include terrain, climate, and
socioeconomic variables, which are measured at a scale of 10 by 10 kilometers, except
for socioeconomic data that are measured at county level – the most disaggregated unit
available.
11
Figure 2.2. Distribution of Land-use Change on Grassland, 1988–2000.
Discrete dependent variable models have been widely applied to the studies of
land use and land-use change (Carrión-Flores and Irwin 2004; Lewis and Plantinga 2007;
Lubowski et al. 2006; Nelson et al. 2001; Nelson and Hellerstein 1997; Polyakow and
Zhang 2008; Wu et al. 2004; Wu and Cho 2007), where spatial autocorrelation are an
important econometric concern because land uses are spatially distributed. The cost of
not correcting for spatial dependence is inefficient and/or inconsistent estimates if the
error structure or land-use choice is correlated over space (Anselin 2006). However, in
the context of limited dependent variable model, it is technically challenging to overcome
computational burdens when dataset is large. Some studies employ spatial sampling
technique to solve this problem (Carrión-Flores and Irwin 2004); others construct spatial
lags as instrumental variables for the right hand side of equation (Nelson et al. 2001;
Nelson and Hellerstein 1997). But most of the literature ignores the potential spatial
interdependence.2 In this study, we adopt two approaches, including an explicitly spatial
12
multinomial logit model, to correct for the potential endogeneity resulting from spatial
autocorrelation in the dependent variable.
This essay departs from previous studies in two aspects. First, we use unusually
detailed, national-scale land use data, which were developed based on the US Landsat
image with a spatial resolution of 30 by 30 meters. To the best of our knowledge, it is the
first economic application in land use literature. Previous econometric studies on China
typically aggregate land use data from pixel level to county level. The aggregation
provides contiguous coverage of land conversion in a region but does not provide
information on the spatial pattern of land-use change within a county. In contrast, this
study uses data at highly disaggregated level, which helps understand the emergence of a
collective pattern of land-use change where interdependence between land use choices is
an important element. Second, we apply a new econometric technique in multinomial
logit regression, which allows for modeling spatial autocorrelation explicitly with a large
dataset.
The remainder of this essay is organized as follows. Section 2 describes the
land-use change model. Section 3 discusses data. Section 4 reports the estimation results.
Section 5 and Section 6 present simulation results. The final section concludes.
13
THE MODEL
To model land-use change in China, we must fully understand China‟s
landownership. Unlike the United States and many European countries, China has no
private land. Land can be owned by the state or by village collective, depending on land
use types. For example, all urban land and most forest, pasture, water area, and unused
land belong to the state; and all farmland is collectively owned by villagers. Land use is
also heavily regulated by the government. The state retains the right to requisition
farmland and other collectively owned land for urban construction, industrial
development, and transport infrastructure by paying subsidies to villagers based on the
original use of the land. Land requisition is the single type of land ownership transaction.3
Empirical Specification
In this context of landownership, land use decision can be made by two types of
agents – government (county-level or above) and village collective. They have different
concerns: government officials are interested in their political and economic
achievements to get more promotion opportunities, whereas individual villagers care the
net returns to land. We assume that each type of (risk-neutral) agent makes land use
decision to maximize utility. Based on their concerns, the utility of government includes
the level of local GDP and image-building projects; while the utility of villagers comprise
household income and employment opportunity. There are six alternative uses for each
parcel of land: farmland, grassland, forestland, water area, urban area, and unused land.
Let k and s be initial and final land use, respectively. We assume that urban
development is irreversible, i.e., urban area will never be converted to nonurban uses.
Therefore k can be any of five nonurban uses and s can be any of all six uses.
Let U is|k denote the agent‟s utility from converting land grid i from use k to use
s. U is|k can be decomposed into a deterministic component and an unobserved random
component: Uis|k  Vis|k   is|k . The key variables affecting the deterministic component
14
Vis|k are identified based on urban and land economics theory. In a monocentric open city,
the market land curve equals the upper envelope of the equilibrium bid rent curve of
household at each location:
(2.1)
R t, r     I t   T  r  ,
where r is the distance from CBD, T  r  is the transport cost at r , and I  t  is the
household income at time t .  I  0, T  0, and Tr  0 . Although the assumption of
competitive land market does not hold in China, Deng et al. (2008b) show that the
monocentric city model has fairly high explanatory power when applied to China. So we
adopt equation (2.1) to motivate the empirical specification of urban land rent. In a
perfectly competitive market, rent in agricultural land equals revenue minus costs of
other inputs per unit land given long-run profits is zero. Von Thünen‟s theory on
agricultural land rent (Hall 1966) serves the theoretical basis of farmland bid rent.
Based on the economic theories, we use five pixel-level geophysical variables
and four county-level socioeconomic variables to construct Vis|k . The geophysical
variables are land productivity, precipitation, temperature, the temporal variations in
precipitation and temperature, respectively. These variables, discussed in details in the
data section, measure agricultural yield potentials. For example, land productivity is
estimates of crop yield and climate variables are supplements to land productivity.4 Three
more pixel-level geophysical variables designed to capture spatial effects are discussed
below. The socioeconomic variables are county GDP, population, public agricultural
investment, and highway density. Specifically, population captures the effect of
household income together with county GDP, highway density measures transport costs
for conveying agricultural products, and public agricultural investment contributes to
improving agricultural productivity in the long run.
The unobserved random component  is|k is assumed to follow a type-I extreme
value distribution. Under this assumption, the probability of converting land grid i from
use k to use l is:
15
Pil|k  Pr U il|k  U is|k ,  l  s 
 Pr Vil|k   il|k  Vis|k   is|k ,  l  s 
.
 Pr   is|k   il|k  Vil|k  Vis|k ,  l  s 
Vil|k
 e Vis|k
se
(2.2)
Equation (2.2) defines a multinomial logit regression model for each starting use k . To
avoid redundant parameters, we set the initial use k as reference and normalize the
corresponding coefficients to zero‟s such that Vik |k  0 . Hence there are five probability
equations in the regression for each starting use k . We use maximum likelihood method
to maximize the joint probability of multiple land-use choices based on equation (2.2).
Econometric Issues
Spatial autocorrelation is an important econometric concern when applying
contiguous geographic data for empirical analysis. The cost of not correcting for spatial
dependence is inefficient and/or inconsistent estimates if the error structure or land-use
choice is correlated over space. But in practice it is technically challenging to distinguish
between two types of spatial autocorrelation. Further, true residuals are unobservable in a
limited dependent variable model, which makes it more difficult to test against spatial
autocorrelation. Kelejian and Prucha (2001) develop a generalized Moran‟s I statistic
(asymptotically equivalent to a Lagrange Multiplier statistic) that can be used to examine
the existence of spatial error correlation. However, the econometric theory of testing for
spatial interdependency of discrete LHS variable is still in its infancy in the literature.5 In
this essay we ignore the potential for spatial dependence in error term because the
estimates would be asymptotically efficient when the data sets used in estimation are
extremely large.6 To correct for the potential endogeneity resulting from spatial
autocorrelation in the dependent variable, we experiment with the following two
approaches.
In the first approach, we add three geophysical variables – terrain slope,
elevation, and neighborhood index – as instruments to the right hand side (RHS) of the
utility equation. We adopt a regular structure (i.e., an unlagged form) of terrain slope and
16
elevation instruments, which differs from the previous studies which use RHS spatial lags
in the spatial analysis (Nelson et al. 2001; Nelson and Hellerstein 1997). Terrain slope
and elevation used in this essay are able to capture the information from grids adjacent to
the original location because they are generated from China‟s digital elevation model
(DEM). DEM has taken spatial effects into account when estimating or retrieving the
values of other locations during the interpolation process. Neighborhood index is
constructed based on a six-dimensional vector, measuring the average of the percent land
coverage of the eight cells surrounding the original location for each use. It is of
theoretical significance to include neighborhood index in the utility equation. For
example, in the local jurisdictional models, the land rents in the same community are
correlated because they are affected by local public services providing by the local
jurisdiction. The surrounding urban use coverage may serve as a proxy for the
neighborhood effects.
Hence the deterministic component of utility Vis|k can be written as
(2.3)
Vil|k  V  xil , y i , z m   l|k  xill|k  y i βl|k  z mγ l|k ,
where lk is transition-specific constant capturing conversion costs.; xil is the
neighborhood index; y i is a vector of variables describing the locational characteristics
of grid i, such as soil quality, topographic features, and weather conditions; and z m is a
set of socioeconomic variables indexed by county m in respect that county is the most
disaggregated unit available for measuring socioeconomic data. Since the absolute
magnitude of coefficient in a multinomial logit model has no economic interpretation, we
set initial use in k as reference and normalize the coefficients so that k |k  0 ,  k |k  0 ,
β k |k  0 , and γ k |k  0 , as we discussed in the previous section. The normalization avoids
an overidentification issue in the regression.
Building on the first approach, the second approach models the spatial
autocorrelation explicitly. Specifically, we develop a spatial multinomial logit model by
assuming that agents‟ utilities are spatially dependent.7 Having surrounding land in the
17
same use could help lower maintenance costs, encourage government to invest in
infrastructure; agents may also benefit from knowledge spillover. In these situations, net
returns to adjacent land parcels are correlated. Therefore, we add a spatially lagged utility
to the RHS of the utility equation such that
(2.4)
U   WU  V  ε ,
where  is a spatial autoregressive parameter (   1 ). The magnitude of  represents
the extent to which an element of LHS variable ui is affected by the remaining elements
u j for j  i . Thus the standard model is a special case of the spatial model when   0 .
W is a row-standardized n  n weight matrix such that wii  0 and

n
j 1
wij  1 . We
specify the (i,j)th entry of the weight matrix W as a Gaussian function of geographical
distance from location j to location i as equation (2.5) shows.8
(2.5)
wij  exp   dij2 h2 

n
j 1
exp   dij2 h2  ,
 i, j  1,
, n, and i  j ,
where d ij measures the Euclidean distance between location i and j , and h is referred to
as the bandwidth. The reduced form of equation (2.4) is given by
(2.6)
U  I   W V  I   W ε .
1
1
For notational convenience, let V*   I   W  V . Now the expression of probability of
1
converting grid i from land-use k to land-use l is
(2.7)
Pil|k 
Vil*|k
e
 s e is|k
V*
.
In practice it is infeasible to conduct estimation in the context of limited
dependent variable regression when dataset is large, because evaluating a log-likelihood
function needs an n-dimensional integration where n is sample size. To overcome this
problem, Pinkse and Slade developed a generalized method of moments, which works for
a spatial error model. But it is still technically impracticable to apply this approach in a
spatial lag model. Recently, Klier and McMillen revised Pinkse and Slade‟s method by
developing a linearized9 logit version in a spatial lag framework, which simplifies the
18
algorithm to only two steps – a standard logit estimation followed by a (linear) two-stage
least squares regression. Hence it is feasible to estimate a logit model with spatially
lagged dependent variables in a large dataset. This study follows this strand and extends
Klier and McMillen‟s approach to the context of multinomial logit regression. To the best
of our knowledge, no published studies have done this before. However, as we have
discussed, there are no formal results in the literature so far that can test for the existence
of spatial interdependency of discrete LHS variable. Therefore we directly test against the
null hypothesis of   0 using Student‟s t statistics reported in the two-stage least
squares regression. If the test rejects the null, then spatial endogeneity exists in the
dependent variable.
Another difficulty with the spatial regression is that the estimated parameters are,
in part, functions of the weighting function. As the bandwidth h tends to infinity, the
weighting function exp   dij2 h2  is close to one for all pairs of points so that
wij   n  1
1
 j  i . Equivalently, the weight becomes uniform for every point j no
matter how far it is from location i . Conversely, as h becomes smaller, utility will
increasingly depend on observations in close proximity to i . In particular, the weighting
function exp   dij2 h2  tends to zero when the distance d ij exceeds approximately 2.15
times as long as the bandwidth h . The problem hence becomes how to select an
appropriate bandwidth or decay function in regression. In this study we assume a uniform
h for all models and choose h on a criterion of minimum Predicted Residual Error Sum
of Squares (PRESS), where the fitted value with the point i omitted from the calibration
process.
In the remaining of this essay, we refer to the model developed based on the first
correction as the standard multinomial logit model, and refer to the model estimated by
using the second approach as the spatial multinomial logit model.10
19
DATA
Our study covers Mainland China. Most data used in this essay were provided
by the Chinese Academy of Sciences (CAS), including land-use type, terrain, climate,
and socioeconomic data. They are contiguous data measured at a scale of 10 by 10 square
kilometers, except for socioeconomic data, which are measured at county level.
Contiguous data are more desirable than dispersed sample plots in the prediction of land
conversions. Table 2.1 provides a detailed summary of the data.
Table 2.1. Summary Statistics of Explanatory Variables
Variable
M easurement Unit
10-km-gird level
Land productivity
Terrain slope
Elevation
Precipitation, 1991-1995
Precipitation, 1996-2000
Std. of precipitation, 1991-1995
Std. of precipitation, 1996-2000
Temperature, 1991-1995
Temperature, 1996-2000
Std. of temperature, 1991-1995
Std. of temperature, 1996-2000
g/ha.
degree
km
1000 mm
1000 mm
1000 mm
1000 mm
degree Celsius
degree Celsius
degree Celsius
degree Celsius
county level
highway
GDP, 1989
GDP, 1996
GDP, 2000
Population, 1989
Population, 1996
Population, 2000
Agricultural investment, 1994
Agricultural investment, 1995
Agricultural investment, 1999
Agricultural investment, 2000
m/10000 ha.
billion RM B yuan
billion RM B yuan
billion RM B yuan
million people
million people
million people
million RM B yuan
million RM B yuan
million RM B yuan
million RM B yuan
N
M ean
Std. Dev.
M inimum M aximum
93902
94662
94612
94173
94173
94173
94173
94173
94173
94173
94173
1.413
3.555
1.837
0.468
0.478
0.081
0.081
6.298
6.677
0.385
0.599
2.632
5.010
1.742
0.421
0.436
0.077
0.067
8.021
8.045
0.105
0.153
0.000
0.000
-0.153
0.006
0.006
0.002
0.002
-17.740
-17.000
0.084
0.239
14.168
72.790
7.040
1.877
1.824
0.402
0.368
31.420
31.620
0.904
1.693
2331
2236
2247
2251
2331
2332
2333
2138
2137
2143
2140
1.022
1.351
2.593
3.956
0.468
0.510
0.529
0.073
0.076
0.077
0.096
3.794
3.579
6.518
11.121
0.456
0.499
0.514
0.379
0.410
0.423
0.526
0.000
0.016
0.021
0.041
0.005
0.006
0.006
0.000
0.000
0.000
0.000
155.708
116.195
202.418
364.877
10.228
10.616
10.817
11.783
13.055
13.653
17.057
Land-use data are generated from a unique land cover and land use database,
which was developed based on the US Landsat TM/ETM images with a spatial resolution
of 30 by 30 meters (Deng et al. 2008a; Liu et al. 2003). The data are available for three
years – the late 1980s, the mid-1990s, and the late 1990s, denoted as 1988, 1995, and
20
2000, respectively. CAS made visual interpretation and digitization of TM images to
generate thematic maps of land cover, and sorted the data with a hierarchical
classification system of 25 land cover classes. Further, CAS grouped 25 classes of land
cover into 6 aggregated classes of land use, i.e., farmland, forestland, grassland, water
area, urban area11, and unused land. Deng et al. (2006) provides a detailed explanation of
the six land-use types.
Table 2.2a. Land-use Transitions from 1988 to 1995
Initial land-use
Farm
Forest
Grass
Water
Urban
Unused
Freq
Prob
Freq
Prob
Freq
Prob
Freq
Prob
Freq
Prob
Freq
Prob
Total
Farm
11,131
0.662
2,787
0.125
1,931
0.064
415
0.155
106
0.321
246
0.012
16,616
Forest
2,952
0.176
15,976
0.719
2,974
0.099
179
0.067
29
0.088
312
0.016
22,422
Final land-use
Grass
Water
1,947
386
0.116
0.023
2,997
161
0.135
0.007
21,333
336
0.709
0.011
400
1,353
0.150
0.506
16
10
0.048
0.030
3,142
329
0.157
0.016
29,835
2,575
Urban
178
0.011
36
0.002
16
0.001
28
0.010
160
0.485
11
0.001
429
Unused
212
0.013
272
0.012
3,518
0.117
298
0.111
9
0.027
16,026
0.799
20,335
Total
16,806
1
22,229
1
30,108
1
2,673
1
330
1
20,066
1
92,212
Urban
100
0.006
24
0.001
11
0.000
12
0.005
305
0.696
7
0.000
459
Unused
152
0.009
253
0.011
2,630
0.088
268
0.103
4
0.009
16,665
0.819
19,972
Total
16,636
1
22,469
1
29,861
1
2,596
1
438
1
20,359
1
92,359
Table 2.2b. Land-use Transitions from 1995 to 2000
Initial land-use
Farm
Forest
Grass
Water
Urban
Unused
Total
Freq
Prob
Freq
Prob
Freq
Prob
Freq
Prob
Freq
Prob
Freq
Prob
Farm
12,531
0.753
2,344
0.104
1,720
0.058
235
0.091
85
0.194
188
0.009
17,103
Forest
2,122
0.128
17,422
0.775
2,261
0.076
97
0.037
15
0.034
204
0.010
22,121
Final land-use
Grass
Water
1,478
253
0.089
0.015
2,281
145
0.102
0.006
22,937
302
0.768
0.010
248
1,736
0.096
0.669
9
20
0.021
0.046
3,025
270
0.149
0.013
29,978
2,726
21
Table 2.2a-2.2b describe land transition matrices of six land-use classes for the
time intervals of 1988-1995 and 1995-2000. Land-use exchanges mainly occur between
farmland, forestland, and grassland, as well as between grassland and unused land. Urban
area expansion is not as significant as anticipated when viewed from a national
perspective.
Data on geophysical variables are generated from a geographical information
system (GIS) database, including cross-sectional data of land productivity, terrain slope,
and elevation. Land productivity is a pixel-specific (5-kilometer-grid) variable, originally
estimated by a research team from Institute of Geographical Sciences and Natural
Resources Research, CAS by using standalone software of Estimation System for the
Agricultural Productivity (Deng et al. 2006). Terrain slope and elevation are generated
from China‟s digital elevation model as part of the basic CAS database.
Climate panel data are initially collected from over 400 weather stations and
organized by the Meteorological Observation Bureau of China. The dataset includes
mean annual precipitation and mean annual temperature from 1991 to 2000, CAS
interpolated the point climate data into surface data with the method of thin plate
smoothing spline (Hartkamp et al. 1999) to get more disaggregated information for each
pixel. We calculate the standard deviations of mean annual precipitation and mean annual
temperature along time, and use them as measures of temporal variations in climate.
Socioeconomic variables, such as county GDP and population are gathered from
several versions of statistical yearbooks and population yearbooks for China‟s counties
and cities for three years (1989, 1996, and 2000). A common suggestion is that county
GDP and population are “endogenous.” We use lagged county GDP and population
measures (1989, 1996), so that statistical endogeneity seems less likely. Data on public
agricultural investment are collected from province and county level statistical yearbooks
for four years (1994, 1995, 1999, and 2000). The investment comes from fiscal budget of
the state and local government. It is mainly used for developing agriculture infrastructure
like seeds, fertilizers, and irrigation. Data on highway density are available for one year.
Based on a digital map of transportation networks in the mid-1990s, highway density are
22
calculated as the total length of all highways in a county divided by land area of that
county. Data in value terms are measured at the 2000 real Chinese yuan (hereafter CNY
or ¥). All of these variables are county-level data.
23
RESULTS
We estimate the standard and the spatial multinomial logit models separately
with a dataset composed of observations at a 10-km-land-grid scale. There are two
transition periods, 1988-1995 and 1995-2000, for the analysis. During each period there
are five initial land uses (farmland, forestland, grassland, water area, and unused land)
and six final uses (farmland, forestland, grassland, water area, urban area, and unused
land). So we estimate twenty separate models in total.12
We apply maximum likelihood method to estimate ten standard multinomial
logit models. The pseudo R2 (McFadden's likelihood ratio index) ranges between 0.546
and 0.825. Estimation of ten spatial multinomial models is conducted using the linearized
generalized method of moments. Appendix A provides a detailed description of the
algorithm. The estimated value of the bandwidth h is 200 km. The essential idea behind
the bandwidth is that for each location i there is a circle centered at i with a radius of
430 km ( 430km  200km  2.15 ). Within the circle points around i have “bump of
influence” on i ; beyond the circle the influence of points are negligible.
To save space, we will report and discuss the estimation results of land-use
change on farmland and grassland for two transition periods, presenting the remaining
results in the Appendix B. Table 2.3a-2.3b report coefficient estimates for the standard
model of land conversion on farmland, respectively, from 1988 to 1995 and from 1995 to
2000. Estimates and standard errors of parameters in equation (2.3) are presented in each
column by land-use choice. It shows that the sign, magnitude, and statistical significance
of estimates are consistent in transition period and are in line with the economic
interpretation on the whole. For example, all transition-specific constants have negative
estimates and almost all of them are statistically different from zero at the 1% level,
indicating that conversion cost deters land conversion on farmland. Likewise, the
estimates of land productivity are negatively significant, implying that a patch of
farmland with higher crop yield potential is less likely to be changed to other uses. There
is evidence that the odds of farmland conversion are associated with climate, e.g., a patch
24
of high-rainfall farmland is more likely to be afforested and a patch of low-rainfall and
low-temperature farmland is less likely to be abandoned.
The results provide strong evidence for the association between the odds of
farmland conversion and county-level socioeconomic factors, such as county GDP,
population, agricultural investment, and highway density. It is particularly significant
during the transition period of 1988-1995. As shown in Table 2.3a and 2.3b, land is more
likely to be changed out of farming in a county with higher level of GDP. With higher
GDP, the demand for residential development and industrial and commercial uses
increases. Farming is generally a low-paying job in China, so farmers are willing to be
engaged in other higher-returned activities rather than farming. Conversely, farmers are
more likely to farm in a county with low GDP for the lack of high-paying jobs. Increased
population may increase labor supply. In a county with large population, farmland is less
likely to be converted to other uses because farming is still the main way to make a living
if the industry is underdeveloped. Likewise, public agricultural investment contributes to
improving agricultural productivity in the long run, which decreases the probabilities of
farmland conversions. In the empirical model of this study, highway density measures
freights of conveying agricultural products. Dense highway tends to lower transport costs,
which decreases the probabilities of farmland conversions. The GDP coefficient on urban
expansion is not as statistically significant as anticipated, especially in the second period.
For one reason, urban development mainly occurred in East China. Therefore GDP only
has moderate effects on urban expansion if viewed from the whole country. For another,
the neighborhood effect, as a proxy for accessibility (or location) rent is so large that it
outperforms the role of GDP in encouraging urban development.
Table 2.4a-2.4b report estimated parameters of the spatial multinomial model of
land conversion on farmland for the periods of 1988-1995 and 1995-2000.13 Likewise,
the sign, magnitude, and statistical significance of estimates are generally consistent for
the two periods. The spatial autoregressive parameter (  ) is estimated to be 0.0760 and
0.3308 and is statistically significant at 5% and 1% levels for two transition periods,
respectively. Positive estimates imply positive spatial externalities of land-use choices
Table 2.3a. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Farmland, 1988-1995
Indep. Variable
Forestland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.4944*** (0.2027)
-2.4109*** (0.2505)
-4.0919*** (0.4224)
-6.1098*** (0.7112)
Land productivity
-0.0570*** (0.0122)
-0.0959*** (0.0144)
-0.0577*** (0.0207)
-0.0457*
(0.0270)
County GDP
0.0479***
(0.0167)
0.0623***
(0.0202)
0.0706**
(0.0293)
0.0944***
(0.0332)
Population
-0.3910*** (0.1031)
-0.6637*** (0.1354)
-0.2232*
(0.1271)
-0.1864
(0.1854)
Agricultural investment
-0.1021
(0.1508)
-0.0822
(0.1196)
-0.5175
(0.4002)
-0.7971**
(0.3207)
Highway density
-0.1156*** (0.0296)
-0.1249*** (0.0464)
0.0040
(0.1078)
0.0429
(0.0534)
Terrain slope
0.0541***
(0.0104)
0.0799***
(0.0108)
0.0169
(0.0338)
-0.1900
(0.1186)
Elevation
0.0950*
(0.0576)
0.1758***
(0.0574)
-0.2773
(0.1733)
0.3259
(0.2554)
Precipitation
0.9175***
(0.1924)
-0.0651
(0.2312)
-0.7200*
(0.4004)
0.4239
(0.5523)
Temperature
-0.0065
(0.0099)
0.0142
(0.0105)
0.0889***
(0.0244)
0.1137**
(0.0452)
Std Err of precipitation
-2.4573*** (0.6117)
-0.6045
(0.9496)
0.0051
(1.1433)
-0.2298
(1.7038)
Std Err of temperature
-0.9903*** (0.3680)
-0.2820
(0.4576)
-0.5643
(0.8490)
-2.6193**
(1.1561)
Neighborhood index
0.0511***
(0.0012)
0.0519***
(0.0015)
0.0869***
(0.0032)
0.1032***
(0.0048)
Number of observations
15012
M cFadden's LRI
0.6436
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-0.9629
(0.7185)
-0.1269*** (0.0320)
-0.0004
(0.0905)
-0.2542
(0.2555)
-1.0209
(1.2504)
0.0370
(0.1292)
-0.0129
(0.0818)
-0.7721*** (0.2108)
-3.0655*** (0.8473)
-0.0753*** (0.0223)
-0.3552
(4.8060)
-0.7981
(1.3608)
0.0530***
(0.0032)
25
Table 2.3b. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Farmland, 1995-2000
Indep. Variable
Forestland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-3.8590*** (0.2606)
-3.2968*** (0.2424)
-4.9410*** (0.6075)
-7.2280*** (0.9423)
Land productivity
-0.1027*** (0.0136)
-0.1318*** (0.0157)
-0.0510**
(0.0233)
-0.0463
(0.0368)
County GDP
0.0026
(0.0081)
-0.0045
(0.0146)
0.0315*
(0.0166)
0.0114
(0.0226)
Population
0.0379
(0.0947)
-0.3445*** (0.1274)
-0.2059
(0.2344)
0.0661
(0.2134)
Agricultural investment
-0.1981*
(0.1019)
-0.0524
(0.1141)
-0.2718
(0.2381)
-0.1176
(0.2517)
Highway density
-0.1610*** (0.0504)
-0.1808*** (0.0635)
-0.1645
(0.1513)
-0.0802
(0.1757)
Terrain slope
0.0726***
(0.0091)
0.0498***
(0.0110)
-0.2091*** (0.0585)
-0.0871
(0.1163)
Elevation
0.0780
(0.0591)
0.4166***
(0.0643)
-0.0817
(0.1958)
0.4502*
(0.2462)
Precipitation
0.6961***
(0.1513)
-0.3161*
(0.1863)
0.8743**
(0.3844)
0.5525
(0.5124)
Temperature
-0.0020
(0.0101)
0.0221*
(0.0116)
0.0040
(0.0290)
0.0392
(0.0422)
Std Err of precipitation
0.3500
(0.5999)
0.3058
(0.9794)
-0.6821
(1.7468)
-1.5711
(2.9525)
Std Err of temperature
1.0990***
(0.3650)
1.0649***
(0.2832)
1.2361
(0.8399)
1.1539
(1.1732)
Neighborhood index
0.0419***
(0.0013)
0.0383***
(0.0015)
0.0567***
(0.0037)
0.0980***
(0.0064)
Number of observations
14794
M cFadden's LRI
0.6662
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-0.4472
(0.8303)
-0.1482*** (0.0411)
0.0429**
(0.0208)
-0.7696
(0.5879)
0.1167
(0.3628)
-0.1332
(0.1735)
-0.2446*
(0.1289)
-0.3200
(0.2467)
-4.3673*** (1.0255)
-0.1388*** (0.0324)
1.3712
(3.3825)
-0.6066
(1.1337)
0.0438***
(0.0040)
26
Table 2.4a. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Farmland, 1988-1995
Indep. Variable
Forestland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.4164*** (0.1622)
-2.6617*** (0.2012)
-3.8213*** (0.7071)
-5.4724*** (1.6243)
Land productivity
-0.0680*** (0.0077)
-0.1186*** (0.0090)
-0.0152
(0.0962)
0.2871
(0.6642)
County GDP
-0.0997**
(0.0467)
-0.1016**
(0.0494)
-0.0788
(0.4152)
0.1159
(0.8281)
Population
0.3707***
(0.0099)
-0.6283*** (0.0132)
-0.2020*** (0.0314)
-0.1826*** (0.0599)
Agricultural investment
-0.1800*** (0.0404)
-0.0529
(0.0437)
-0.5279*** (0.1216)
-0.8027*** (0.0700)
Highway density
-0.1230*** (0.0120)
-0.1316*** (0.0186)
-0.0056
(0.0372)
0.0135
(0.0405)
Terrain slope
0.1243
(0.0860)
0.0883
(0.1428)
0.0815
(0.1703)
-0.0204
(0.3133)
Elevation
0.1127
(0.0860)
0.2020*
(0.1081)
-0.1642
(0.5866)
0.4496
(0.4085)
Precipitation
0.7794***
(0.1449)
0.2288
(0.1828)
-0.4695
(0.6419)
1.2320
(1.3165)
Temperature
-0.0053
(0.0070)
0.0034
(0.0078)
0.0743*
(0.0420)
0.0277
(0.1018)
Std Err of precipitation
-2.7082*** (0.4249)
-1.6864**
(0.8138)
-0.7702
(1.6734)
-1.1668
(3.5181)
Std Err of temperature
-0.5423**
(0.2733)
0.7490**
(0.3310)
-0.5931
(1.2747)
-1.9306
(2.4402)
Neighborhood index
0.0520***
(0.0009)
0.0534***
(0.0011)
0.0866***
(0.0040)
0.1036***
(0.0093)
Spatial parameter (ρ)
0.0760**
(0.0333)
Number of observations
15012
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
1.1830
(1.1756)
0.0027
(0.2380)
0.0701
(0.4799)
-0.2442*** (0.0819)
-1.0304*** (0.1476)
0.1581
(0.1877)
0.4488
(0.9424)
-2.2122
(2.6257)
-1.9152
(2.3880)
-0.0731
(0.0491)
-11.4784
(9.9609)
-3.0681*
(1.8193)
0.0520***
(0.0032)
27
Table 2.4b. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Farmland, 1995-2000
Indep. Variable
Forestland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.4895*** (0.2394)
-2.1248*** (0.1949)
-3.0390*** (1.103)
-6.4762**
(2.6449)
Land productivity
-0.1100*** (0.0056)
-0.1419*** (0.0073)
-0.2288
(0.2453)
0.1070
(0.5066)
County GDP
-0.2073*** (0.0414)
-0.3589*** (0.0493)
0.4184
(0.4735)
0.2532
(0.8937)
Population
0.0745***
(0.0103)
-0.2951*** (0.0145)
-0.2072*** (0.0390)
0.0573
(0.0833)
Agricultural investment
-0.3675*** (0.0509)
-0.1681*** (0.0530)
-0.2051
(0.3024)
-0.2037
(0.2658)
Highway density
-0.1764*** (0.0079)
-0.2601*** (0.0281)
-0.1750*** (0.0180)
-0.0995*** (0.0365)
Terrain slope
0.1755**
(0.0717)
0.2501*
(0.1455)
-0.0581
(0.2787)
0.1157
(0.4991)
Elevation
-0.1064
(0.1267)
0.3102***
(0.1131)
-0.0685
(0.2742)
0.5453
(0.3399)
Precipitation
0.1399
(0.1271)
-0.0453
(0.1428)
0.9428
(0.6093)
1.0594
(1.5738)
Temperature
-0.0109
(0.0069)
0.0035
(0.0078)
-0.0113
(0.0436)
0.0553
(0.1321)
Std Err of precipitation
0.5744
(0.3746)
0.8760
(0.7963)
-0.7223
(2.6062)
-4.5177
(7.4682)
Std Err of temperature
1.1702***
(0.2649)
1.0190***
(0.1764)
0.3942
(1.6958)
2.4109
(3.3430)
Neighborhood index
0.0405***
(0.0008)
0.0353***
(0.0009)
0.0548***
(0.0034)
0.1013***
(0.0132)
Spatial parameter (ρ)
0.3308***
(0.0357)
Number of observations
14794
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-1.9842*
(1.1742)
-0.8823*
(0.5176)
1.0089*
(0.5360)
-0.7455*** (0.1264)
0.3147
(0.2937)
-0.1344*** (0.0383)
-0.4215
(1.2291)
-0.3368
(0.8959)
-0.4517
(2.5570)
-0.0354
(0.0758)
10.6778
(4.7202)
-0.4945
(1.7233)
0.0421***
(0.0032)
28
29
between neighboring locations. Particularly, it demonstrates an increasing trend of spatial
dependence over time. A comparison of results in Table 2.4a-2.4b and Table 2.3a-2.3b
shows that the signs and relative magnitudes of most estimates, such as land productivity,
population, agricultural investment, highway density, neighborhood index, etc., are
robust to the spatial lag specification. Nevertheless, we find that in the utility equation of
converting farmland to unused land, the absolute values of estimated coefficients on
standard error of precipitation are extremely high, indicating that results of spatial
multinomial model are sensitive to some explanatory variables especially when a choice
is least likely to be selected.
In addition to land conversion on farmland, we report coefficient estimates of
land-use change on grassland, for the standard and spatial model, and for two transition
periods in Tables 2.5a-2.6b. Similar to the foregoing outcome presented in Tables 2.3a2.4b, the results are generally consistent in time and robust to the spatial lag specification,
including sign, magnitude, and statistical significance of estimates. For example,
transition-specific constants are estimated to be significantly negative and neighborhood
indices are estimated to be significantly positive at the 1% level. Estimates of land
productivity, population, and highway density are statistically positive in the utility
equations of farmland and forestland, implying that cultivation and afforestation are more
likely to take place on a patch of grassland that possesses higher productivity, larger
population, or denser highway. By contrast, a patch of grassland with lower productivity
or smaller population is more likely to be converted to unused land. Compared with
county GDP, population is a more stable socioeconomic factor in driving grassland
change. In the context of grassland conversion, the magnitude of spatial dependence is
not as time-sensitive as that of change on farmland. Spatial autoregressive parameters (  )
in two periods are respectively estimated to be 0.3709 and 0.3542, which are statistically
significant at the 1% level. Again, in the utility equation of small probability event (e.g.,
urban development in this case), we find unusual coefficients estimates of some
explanatory variables (e.g., intercept, terrain slope, standard error of precipitation and
30
temperature). Hence caution should be exercised when applying the spatial multinomial
model, where the estimates are not robust to small probability events.
Table 2.5a. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Grassland, 1988-1995
Indep. Variable
Farmland
Forestland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.5272*** (0.2438)
-3.2139*** (0.2120)
-4.5855*** (0.4887)
-8.1604*** (2.9041)
Land productivity
0.0782***
(0.0171)
0.1272***
(0.0197)
0.1898***
(0.0508)
0.3214*
(0.1786)
County GDP
0.0527**
(0.0250)
-0.0053
(0.0366)
0.1095*
(0.0574)
0.0575
(0.6718)
Population
0.4507***
(0.1550)
0.8189***
(0.1669)
0.0486
(0.5327)
-0.8714
(3.6464)
Agricultural investment
-0.1943
(0.1893)
0.3088**
(0.1298)
0.4021
(0.4338)
-0.1310
(4.6872)
Highway density
0.1609***
(0.0428)
0.0957
(0.0642)
0.1711
(0.1471)
-0.4573
(0.7783)
Terrain slope
0.0140
(0.0095)
0.0380***
(0.0069)
-0.0609*** (0.0228)
-0.0157
(0.1109)
Elevation
-0.4717*** (0.0510)
-0.1437*** (0.0335)
-0.1110
(0.0848)
0.5892
(0.7513)
Precipitation
0.5282**
(0.2319)
0.7794***
(0.2024)
0.5972
(0.6803)
-0.9467
(3.8030)
Temperature
-0.0039
(0.0105)
-0.0163**
(0.0080)
-0.0669*** (0.0221)
0.2157
(0.2476)
Std Err of precipitation
0.5156
(1.0097)
-1.3059
(0.8522)
1.2753
(2.7736)
-0.2273
(20.728)
Std Err of temperature
-1.0408**
(0.4464)
-0.8175**
(0.4057)
-0.6959
(0.9127)
-3.7185
(8.2997)
Neighborhood index
0.0556***
(0.0015)
0.0583***
(0.0013)
0.0997***
(0.0038)
0.1662***
(0.0339)
Number of observations
17893
M cFadden's LRI
0.6611
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-2.4478*** (0.2899)
-0.1199**
(0.0549)
0.1456***
(0.0426)
-0.5820
(0.4588)
-1.1709*** (0.3279)
0.0610
(0.0844)
-0.0324*** (0.0083)
0.0267
(0.0481)
-1.1148*** (0.4158)
-0.0166
(0.0112)
1.5814
(2.4712)
-1.3545*** (0.4911)
0.0559***
(0.0013)
31
Table 2.5b. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Grassland, 1995-2000
Indep. Variable
Farmland
Forestland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-3.1325*** (0.2143)
-3.4663*** (0.1859)
-5.7489*** (0.7341)
-5.0566
(9.2609)
Land productivity
0.1507***
(0.0147)
0.0755***
(0.0183)
0.2003***
(0.0652)
0.1022
(0.7749)
County GDP
-0.0741*** (0.0170)
-0.0139
(0.0153)
0.0166
(0.0731)
-0.1279
(0.7389)
Population
0.6967***
(0.1522)
0.7112***
(0.1441)
0.3270
(0.8176)
-0.7448
(10.550)
Agricultural investment
0.2416**
(0.1163)
-0.1530
(0.1156)
0.0391
(0.8088)
-0.3704
(31.015)
Highway density
0.2093***
(0.0612)
0.1865***
(0.0625)
0.1130
(0.1575)
0.6222
(0.5172)
Terrain slope
0.0051
(0.0099)
0.0459***
(0.0063)
-0.0908*** (0.0240)
-0.1523
(0.6673)
Elevation
-0.3827*** (0.0568)
-0.1004*** (0.0341)
0.2322
(0.1546)
-2.2314
(8.8000)
Precipitation
-0.0689
(0.1657)
0.0342
(0.1496)
-0.1111
(0.8861)
0.0435
(13.290)
Temperature
0.0363***
(0.0094)
-0.0001
(0.0073)
-0.0044
(0.0313)
0.0735
(0.6435)
Std Err of precipitation
-0.3487
(0.9213)
3.1369***
(0.7336)
0.5856
(4.7753)
0.1013
(65.121)
Std Err of temperature
0.1826
(0.2723)
-0.0810
(0.2547)
0.5449
(0.7414)
-1.6131
(12.739)
Neighborhood index
0.0435***
(0.0015)
0.0398***
(0.0012)
0.0819***
(0.0047)
0.0927
(0.1070)
Number of observations
18116
M cFadden's LRI
0.6157
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-3.8559*** (0.2912)
-0.0911**
(0.0425)
0.1583***
(0.0175)
-1.8065*** (0.3724)
-1.4602*** (0.2927)
-0.7420*** (0.0853)
-0.0359*** (0.0080)
0.3706***
(0.0561)
-2.4384*** (0.3838)
0.1005***
(0.0121)
0.1737
(1.93550
1.9762***
(0.3033)
0.0287***
(0.0012)
32
Table 2.6a. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Grassland, 1988-1995
Indep. Variable
Farmland
Forestland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-1.2389*** (0.2120)
-1.7452*** (0.1945)
-4.1508*** (1.3296)
-15.369
(17.646)
Land productivity
0.0752***
(0.0091)
0.1233***
(0.0048)
0.2215***
(0.0550)
1.2020*
(0.6366)
County GDP
0.4807***
(0.0455)
0.1561***
(0.0258)
0.2479
(0.1884)
-1.9941
(4.0108)
Population
0.4411***
(0.0134)
0.7872***
(0.0162)
0.0936
(0.1232)
-0.8311*
(0.4721)
Agricultural investment
-0.2927*** (0.0363)
-0.4496*** (0.0493)
0.4143***
(0.0878)
-3.1804
(2.4861)
Highway density
0.1770***
(0.0185)
0.1045***
(0.0317)
0.1350**
(0.0683)
0.1944
(1.1951)
Terrain slope
-0.5795*** (0.1284)
0.4184***
(0.1292)
0.8213
(1.3910)
-20.545*
(7.9721)
Elevation
-0.3153**
(0.1375)
-0.0700
(0.1820)
-0.1089
(0.2945)
-0.8731
(11.818)
Precipitation
-0.3434*
(0.1903)
-1.0597*** (0.1809)
-1.1070
(1.6367)
5.9941
(12.212)
Temperature
-0.0129
(0.0087)
0.0103*
(0.0061)
-0.1032*
(0.0583)
1.0981
(0.9376)
Std Err of precipitation
2.0066**
(0.7889)
0.4617
(0.6011)
11.845*
(6.8454)
-74.123
(55.410)
Std Err of temperature
-1.8063*** (0.3588)
-1.0802*** (0.3070)
0.9165
(2.4233)
23.377
(30.939)
Neighborhood index
0.0522***
(0.0011)
0.0556***
(0.0010)
0.0976
(0.0068)
0.2504
(0.1243)
Spatial parameter (ρ)
0.3709***
(0.0257)
Number of observations
17893
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-2.2998*** (0.2716)
-0.1152*** (0.0104)
0.2036***
(0.0470)
-0.4763*** (0.0978)
-1.1561*** (0.0755)
0.0004
(0.0384)
0.5781
(0.5507)
0.6049*
(0.3200)
-0.3755
(0.4613)
0.0040
(0.0103)
2.8330
(3.1432)
-0.2967
(0.4525)
0.0531
(0.0010)
33
Table 2.6b. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Grassland, 1995-2000
Indep. Variable
Farmland
Forestland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.4354*** (0.2097)
-2.2088*** (0.1806)
-4.0181**
(1.7919)
-18.003
(27.936)
Land productivity
0.1509***
(0.0098)
0.0657***
(0.0044)
0.1694*
(0.0947)
1.1325
(2.4068)
County GDP
0.3276***
(0.0574)
0.0264
(0.0266)
-0.0061
(0.3465)
-4.7848
(17.159)
Population
0.6756***
(0.0120)
0.6873***
(0.0157)
0.3905***
(0.1222)
-2.0046**
(0.8319)
Agricultural investment
0.1693***
(0.0494)
-0.3135*** (0.0476)
0.0468
(0.4883)
0.2128
(0.8250)
Highway density
0.2077***
(0.0184)
0.1971***
(0.0140)
0.0411
(0.0957)
1.1293
(1.9200)
Terrain slope
-0.3060**
(0.1320)
-0.2399*
(0.1229)
0.5193
(1.4622)
12.592
(17.755)
Elevation
-0.4028*** (0.0733)
-0.0310
(0.1199)
0.2544
(1.2613)
-45.132
(58.018)
Precipitation
-0.3070**
(0.1377)
-0.7090*** (0.1323)
0.4320
(1.9937)
-12.234
(12.927)
Temperature
0.0136
(0.0090)
0.0052
(0.0058)
-0.0433
(0.0825)
0.7652
(0.9008)
Std Err of precipitation
1.1634
(0.7682)
1.8629***
(0.5421)
0.5036
(11.469)
61.710
(59.686)
Std Err of temperature
0.4735**
(0.2226)
0.4435*
(0.2338)
0.3880
(1.9510)
17.952
(54.206)
Neighborhood index
0.0385***
(0.0011)
0.0367***
(0.0009)
0.0791***
(0.0067)
-0.1580
(0.1477)
Spatial parameter (ρ)
0.3542***
(0.0330)
Number of observations
18116
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-3.5409*** (0.2751)
-0.0991*** (0.0102)
0.0412
(0.0601)
-1.9724*** (0.0732)
-0.9308*** (0.1529)
-0.7098*** (0.0153)
-0.6200
(0.4491)
0.7635**
(0.3426)
-0.9096**
(0.4186)
0.0794***
(0.0119)
-0.6759
(1.9727)
2.7153***
(0.2338)
0.0281***
(0.0008)
34
35
DRIVERS OF LAND-USE CHANGE
Although the results in Tables 2.3a-2.6b demonstrate the significance of
explanatory variables in land-use change decisions, they say little about the relative
importance of their influences. Due to the nonlinear, multinomial form of the model,
answer to the second question – what are the relative importance of socioeconomic
drivers – can be discerned only through a series of simulations. We use the empirical
standard multinomial logit models14 to predict national land-use change by uses from
1988 to 2000 under a factual and four counterfactual scenarios. As described in Table
2.7a, the factual simulation uses actually historical observations; it provides a benchmark
to measure land use changes under counterfactual scenarios; the counterfactual scenarios
respectively hold county GDP, population, agricultural investment, and neighborhood
index at a hypothetical level and keeps the remaining variables at their historically
observed values.
Table 2.7a. Description of Simulation Scenarios
Scenario
Description
Factual
All variables at actual values
No change in GDP
Fix county GDP at 1989 values
No change in population
Fix population at 1989 values
No agricultural investment
Restrict agricultural investment coefficients to be zero
No neighborhood effects
Restrict neighborhood index coefficients to be zero
Simulations are run at a 10-km-land-grid scale (equivalent to 10,000 hectares).
Change in the total area for each use between 1988 and 2000 is estimated in the following
five steps: 1) using the models for the period of 1988-1995 to predict the probabilities of
land-use choice of every gird in 1995, given the historical use in 1988;15 2) using the
models for the period of 1995-2000 to estimate probabilities of land-use choice of each
grid in 2000, conditional on six uses in 1995; 3) multiplying the probabilities predicted in
the first step by the conditional probabilities predicted in the second step, and hence
obtaining the joint probabilities of land-use choice in 2000 for every individual land cell;
36
4) summing the choice probabilities by land-use type across individuals and multiplying
the summations by 10,000 hectares; 5) calculating the difference between aggregate
hectare of each use estimated in the fourth step and the historical land-use hectare in 1988.
The procedure is applied to every scenario. For each use, we divide the outcome of the
factual simulation, obtained from the fifth step, by the actual land area in 1988 and then
use these ratios to evaluate the performance of the simulation model.
On the whole, the simulation model performs moderately well in regenerating
the direction and relative magnitudes of land-use changes from 1988 to 2000. Except for
a bit underestimation of unchanged land area by -13.68% to -5.71%, errors of the
factually-simulated changed area are within a range of 0.03-5.72% for all uses. Table
2.7b reports the simulation results, where change in hectare is the total simulated land
area change for each use between 1988 and 2000, and percent change is calculated by
subtracting the factually-simulated hectare change from the counterfactually-simulated
hectare change, and divided the difference by the factually-simulated hectare change. It
measures the direction and relative magnitudes of counterfactual scenario to the factual
scenario. In particular, positive (negative) value of percent change indicates that the
factor contributes to decreasing (increasing) the land hectare for that use.
The simulation results reveal that socioeconomic factors have large impacts on
land-use change. As we have discussed in the previous section, GDP growth leads to
farmland loss (i.e., positive percent change) while population growth and agricultural
investment contribute to farmland expansion (i.e., negative percent change). Further, the
magnitudes of GDP and agricultural investment are greater than population, implying that
GDP growth and agricultural investment are more important than population growth in
driving farmland change. In contrast, grassland change tells a different story. Specially,
population growth plays the most important role among three socioeconomic factors,
indicating that it is human activity (particularly farming activity) rather than economic
growth that caused the loss of grassland. One reason is that farming is still a main way for
people to make a living in many underdeveloped regions (e.g., Horqin area, Inner
Mongolia (Zhang and Zhao 2003)).
37
Table 2.7b. Simulated Changes in Land Supplies of Six Major Uses, 1988-2000a
No change in
No change in
Change in major land use
Factual
county GDP
population
Farmland
(1,000 ha.)
1,274.5
1,847.8
991.8
%
0.0%
45.0%
-22.2%
Forestland
(1,000 ha.)
-649.9
-518.9
-966.9
%
0.0%
20.2%
-48.8%
Grassland
(1,000 ha.)
-2,320.2
-2,317.2
-2,095.1
%
0.0%
0.1%
9.7%
Water area (1,000 ha.)
-217.1
-445.3
-218.7
%
0.0%
-105.1%
-0.7%
Urban area
(1,000 ha.)
2,526.7
2,545.4
2,532.6
%
0.0%
0.7%
0.2%
Unused land (1,000 ha.)
-613.8
-1,111.7
-243.8
%
0.0%
-81.1%
60.3%
No agricultural
investment
722.8
-43.3%
-300.4
53.8%
-2,442.2
-5.3%
-220.0
-1.3%
2,604.4
3.1%
-364.6
40.6%
No neighborhood
effects
2,306.0
80.9%
-9,301.7
-1331.2%
-700.5
69.8%
4,310.4
2085.1%
1,597.3
-36.8%
1,788.5
391.4%
a Change in hectare is the total land area change for each use between 1988 and 2000. Percent change is the counterfactuallysimulated net hectare change relative to the factually-simulated hectare change. Positive (negative) value of percent change
indicates that the factor contributes to decreasing (increasing) the land hectare for that use.
We also find that GDP growth and agricultural investment result in deforestation
and population growth contribute to afforestation. In particular, the effect of GDP growth
on forestland is less than effects of other two factors. With regard to change in unused
land, effect of socioeconomic factors generally has opposite tendency against the
foregoing farmland, grassland, and forestland. Specifically, GDP growth tends to expand
unused land because agricultural activities such as cultivation, pasturing, and
afforestation is a relatively low-paying activity as discussed before; while population
growth can shrink unused land since job market is more competitive in a densely
populated area and hence agricultural land will be more valuable; likewise, investment in
agriculture infrastructure such as irrigation can also increase the value of agricultural land.
Therefore, the opportunity cost of converting land out of agricultural use is raised, which
leads to decreases in unused and urban area. Again, the last column of no neighborhood
effects in Table 2.7b confirms the overwhelming effects of spatial externality on land-use
change. Specially, the historical land-use patterns in 1988 and 1995 encourage
afforestation and urban development. This result reveals that the accessibility (or location)
rent, though unobservable, predominates over other rents such as rent on household
income in the equilibrium bid rent structure of China. Hence urban expansion is more
likely to occur in areas with more developed land.
38
PUBLIC AGRICULTURAL INVESTMENT AND FARMLAND PROTECTION
As we have discussed above, public agricultural investment can help protect
farmland from conversion, which is the third question we are going to address – could we
design a cost-effective scheme to preserve farmland from development in traditional
agricultural zones? To answer this question, we select three major agricultural production
regions for the policy analysis: the Huang-Huai-Hai Plain, Yangtze River Delta, and
Sichuan Basin. The regions have 128.08 million hectares of land, covering 700 counties
across 7 provinces and four provincial-level metropolis in China. The three regions are
highly productive agricultural areas, containing 34% of China‟s farmland. From 1988 to
2000, these regions lost 852.77 thousand hectares of farmland and gained 959.35
thousand hectares of developed land. The rapid urban development on high-quality
farmland is threatening food security in China. To protect farmland and food security, the
central government successively constituted and revised the Basic Farmland Protection
Regulation (1994, 1998) and the Land Administration Law (1998, 2004). Apparently,
these policies had only limited success in protecting farmland in these regions.
It is of practical significance to adopt economic incentive as instruments to
supplement the obligatory laws and regulations in farmland preservation. Public
agricultural investment can serve for this purpose. In this section, we design a farmland
protection scheme by increasing public agricultural investment in counties with different
percent of farmland coverage. To this end, we calculate the marginal cost of farmland
preservation for counties with different levels of farmland coverage. Specifically, we
divide county-level farmland coverage percentage from 5% to 75% into seven intervals
uniformly (i.e., 5-15%, 15-25%, …, 65-75%) and use the every midpoint to represent the
interval. We estimate the marginal cost of farmland protection for each interval of
farmland coverage. The formula of marginal cost is given by equation (2.8):
(2.8)
MCI  lim
 I I  I
I 0 A ( I I )  A ( I )

 I I  I
A ( I I )  A ( I )
for small I ,
where A( I ) is the total acreage of farmland in the study area when agricultural
investment is I . We set I and I at ¥20,000/county and ¥5,000, respectively.
39
Figure 2.3. Marginal costs of preserving farmland in counties with different percent farmland
coverage (Study area: the Huang-Huai-Hai Plain, Yangtze River Delta, and Sichuan Basin).
† Marginal costs are measured at an investment level of ¥20,000 per county.
Figure 2.3 illustrates the estimated marginal costs, where the horizontal axis
represents the percent of farmland coverage and the vertical axis denotes the estimated
marginal cost at the investment level of ¥20,000. As is shown there are very large
variations in the costs, with the highest value (¥1282/ha.) being almost six times larger
than the lowest value (¥190/ha.). The marginal cost curve is increasing and convex,
implying that it is more cost-effective to protect farmland in counties with lower
farmland coverage. In particular, 50% (or the range of 45-55%) is a cutoff level, below
which marginal costs are moderately low. The result seems some counterintuitive.
Intuitively, if a county has high farmland coverage, farmland will be less likely to be
converted. Public spending on farmland protection should be more cost-effective in those
counties. Then how to explain this counterintuitive result? Basically, in counties with
high farmland coverage, the propensity to maintain land for agricultural use is already
high. In this situation, even if there were no agricultural investment, farmland would still
have a high tendency to be preserved. Therefore the marginal effect of agricultural
40
investment is relatively small. This result implies that for farmland protection, it is more
cost-effective to make agricultural investment in counties with lower farmland coverage.
The result has important policy implications.
41
CONCLUSIONS
In this essay, we explored three questions: 1) What are the major drivers of landuse changes in China? 2) What is the relative importance of socioeconomic drivers? 3)
How to design a cost-effective scheme to preserve farmland from development? To this
end, we compiled a unique, national-scale dataset that includes high-quality land use data,
and developed two set of multinomial logit models to analyze land-use choice among six
major uses for two time intervals of 1988-1995 and 1995-2000. We answered these
questions by estimating two set of models and generating a series of simulations with the
estimation results.
The estimation results show that both socioeconomic factors (e.g., county GDP,
population, agricultural investment, and highway density) and geophysical variables (e.g.,
land productivity, terrain slope, elevation, and climate-related variables) affect land-use
change on farmland and grassland, while the effect of geophysical variables on
deforestation is more significant than that of socioeconomic factors.16 The spatial analysis
provides convincing evidence for the existence of spatial dependence, where spatial
autoregressive parameters are significantly positive in a range of 0.0104-0.8865. A
comparison of results in the standard and spatial models reveals robust estimates of most
parameters of spatial models with respect to the signs and relative magnitudes.
We found that GDP growth play an influential role in the conversion of
farmland, forestland, and unused land. In particular, GDP growth causes farmland loss,
deforestation, and idleness. On the contrary, population growth shrinks unused land but
enlarges land area for cultivation and forestation. Population growth is also major factor
driving grassland degradation. Public agricultural investment affects all uses except water
area. Specifically, it encourages land use for farming and pasturing.
We also evaluated the effects of alternative variable on land use. GDP growth
and agricultural investment have larger impacts on farmland conversion, while
population growth and agricultural investment are more influential in deforestation and
grassland conversion. Urban development is more sensitive to agricultural investment
rather than county GDP. Nevertheless, access to developed areas dominates the effects of
42
agricultural investment and county GDP on urban expansion, which confirms that spatial
externality is a major determinant of land-use change. It is more cost-effective for
agricultural investment to larger counties with lower farmland coverage (<50%) when the
objective is to reduce farmland conversion.
This study contributes to the literature in two aspects. First, we use unusually
detailed Chinese data in the analysis. To the best of our knowledge, no one has used such
data for economic research in the land use literature. Second, we apply a new
econometric method in multinomial logit regression. The method takes into account
spatial autocorrelation in the dependent variable explicitly, while the traditional approach
does not work. Caution should be exercised when applying the spatial multinomial logit
model in the empirical work, considering the sensitivity of results to small probability
events and to the specification of weight matrix.
43
ENDNOTES
1
There are relevant clauses to protect arable land in the revised Land Management Law.
2
Some studies use sample plot data from the National Resources Inventory (NRI)
database, which are generated by a stratified sampling routine that ensures plots
geographically dispersed.
3
China‟s land market is generally referred to as land-use right market, which emerged
since the amended Constitution legalized land-use right transaction in 1988. It contains
conveyance market and transfer market of land-use right. Conveyance market is a
primary land market, in which transactions occur between government and land users;
transfer market is a secondary land market, in which transactions occur between land
users.
4
We did not use data on net returns to farmland for two reasons: 1) Data quality is
doubtable. 2) Data on net return are measured at county level; while data on land
productivity and climate variables are available at a more disaggregate level.
5
It is because that the test procedure needs to estimate coefficients and spatial
autoregressive parameter simultaneously.
6
The sizes of all datasets are in a range of 1499-19488 observations.
7
This spatial model may have a potential overidentification problem, which is possible to
generate inefficient estimates. Again, this issue is ignored because of large datasets used
in the study. Alternatively, we can develop a spatial multinomial logit regression without
including three instrumental variables in the model. A potential risk is that the alternative
model may suffer from an underidentification problem and result in inconsistent
estimates.
8
The weight matrix can be specified in several forms: the nearest-neighbor matrix, the
binary block matrix, the distance inverse matrix, the negative exponential function, the
Gaussian function, and the Spherical function (Dubin 1998). We adopt a Gaussian
functional form because 1) the entries are continuous and 2) we can estimate the optimal
bandwidth rather than choosing each entry a priori.
9
Linearization of generalized residuals is implemented around the initial estimates of the
standard model, i.e., ρ=0.
10
Another econometric consideration pertains to the IIA property of the standard
multinomial logit model, i.e., the relative odds of choosing l over k are independent of the
other alternatives. Some studies appeal for more general models (e.g., nested logit model
44
and mixed logit model) to relax IIA assumption (Lubowski et al. 2006; Polyakov and
Zhang 2008). But this approach may be infeasible for a large sample, or may lead to
misspecification when a particular nested or mixed logit model is specified. An
alternative approach is to employ Hausman specification test to examine IIA property.
But even in a well-specified model, Hausman test of IIA often reject the assumption
when alternatives seem distinct (Cheng and Long 2007). In our study, it is unsatisfactory
to apply Hausman test given six land-use alternatives, which requires 15 essential tests
for every initial land use (6!/[2!(6-2)!] = 15). In addition, some applications to land use
have demonstrated that IIA assumption is not a serious problem for empirical work
(Lewis and Plantinga; Lubowski et al. 2006; Polyakov and Zhang 2008).
11
Urban area consists of urban core and other built-up area such as roads, mines, and
development zones that are not contiguous with the urban core.
12
Twenty models are two sets of five starting uses in two transition periods for both
standard and spatial multinomial logit specifications.
13
R2 is not reported because it does not have a statistical interpretation in a two-stage
least squares regression.
14
We adopt standard multinomial logit models rather than spatial models for four reasons:
1) The standard model is able to capture spatial lagged effect to some extent by involving
three instrumental variables. 2) The standard model is more robust in the estimation of
small probability events. 3) The spatial model might be sensitive to the form of weight
matrix. 4) Generating probabilities of land-use change in every spatial model needs an
inversion of n by n matrix, which is technically infeasible; alternatively, we may use a
polynomial expansion as a proxy for the inverse operation, but it would increase
prediction errors. Thus we expect that the standard model perform better than the spatial
model in prediction.
15
For any land grid starting in urban uses, the probabilities of converting to other uses
equal zero provided the assumption of irreversible urbanization.
16
We report the estimation results of land-use change on forestland in the Appendix B
(Tables B1-B4).
45
ACKNOWLEDGMENTS
We thank the National Science Foundation of China (70873118) and the
Chinese Academy of Sciences (KZCX2-YW-305-2) for the financial support to generate
the dataset used in this study.
46
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49
APPENDICES
50
APPENDIX A
Let Vi  V  xi , y i , z m   X i θ , where X  1, x, y, z  , θ   μ, α, β, γ  . Let
dP
parameter Γ   θ,   and gradient G 
. Conversion probability of land grid i can be
dΓ
rewritten as:
Pil|k 
(A2.1)
exp Vil**|k 

exp  X il**|k θ 
 exp V   exp  X
s
where X**   I   W  X* and X il*|k 
1
**
sl |k
X il|k
i
s
**
sl |k
θ
. Let Dil|k is an indicator variable, equal to one
if latent variable Uil|k  Uis|k ,  l  s and zero otherwise. The generalized residual uil|k
equals:
uil|k  Dil|k  Pil|k
(A2.2)
When the spatial autocorrelation parameter   0 , for any l , s  k , gradient
terms can be expanded as:
Pil|k
(A2.3)
θl|k
Pil|k
(A2.4)
θ s|k
Pil|k
(A2.5)
k
 Pil|k 1  Pil|k  Xi
  Pil|k Pis|k Xi if s  l
 Pil|k  WX i θl|k   s  k Pis|k  WX i θs|k 
There are four steps in the estimation procedure.
Step 1: Estimate the standard multinomial logit model and assume initial values for Γ 0
 

0
are Γ   θˆ , 0
Step 2: Calculate generalized residuals uil 0|k  Dil|k  Pˆil|k and the gradient terms G  0 based
on Eq. (A2.3)-(A2.5).
Step 3: Regress G on instruments Z   X, WX, WWX, WWWX  and predict Ĝ .
51
0
0
0
0
0
Step 4: Regress uil |k  Gil|k Γ  on Ĝ . Note that uil |k  Gil|k Γ   uil |k  Gil|k θˆ when   0 .


The coefficients are estimated values of parameters Γ  θ,  .
So no large matrices have to be inverted in the algorithm. All it requires is
standard logit followed by linear two-stage least square regression. A more detailed
discussion of the procedure is provided by Klier and McMillen (2008).
Table A2.1. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Forestland, 1988-1995
Indep. Variable
Farmland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.7835*** (0.1834)
-3.0779*** (0.1945)
-4.6856*** (0.7387)
-4.4508*** (1.6021)
Land productivity
-0.0065
(0.0130)
0.0566***
(0.0170)
0.0691
(0.0430)
-0.1788
(0.1189)
County GDP
0.0027
(0.0147)
0.0182
(0.0225)
-0.1406
(0.0966)
0.0157
(0.0574)
Population
-0.0550
(0.0930)
-0.0021
(0.1321)
0.8228**
(0.4039)
0.2045
(0.4704)
Agricultural investment
0.0491
(0.0886)
-0.1197
(0.1150)
0.2975
(0.6051)
1.1349*
(0.6332)
Highway density
0.1676***
(0.0498)
0.1716***
(0.0567)
0.1136
(0.2419)
-0.3792
(0.8857)
Terrain slope
-0.0243*** (0.0072)
-0.0095
(0.0062)
-0.2402*** (0.0435)
-0.2248*
(0.1173)
Elevation
-0.1688*** (0.0500)
0.1285***
(0.0322)
-0.5179*** (0.1666)
-2.8689*** (0.6227)
Precipitation
-0.4353*** (0.1574)
-1.0997*** (0.1715)
-0.7107
(0.6314)
-1.0955
(1.3883)
Temperature
0.0507***
(0.0084)
0.0301***
(0.0068)
0.0771**
(0.0323)
0.0790
(0.0784)
Std Err of precipitation
-1.6625*** (0.4919)
1.3443**
(0.6553)
-0.6897
(1.9371)
-1.0067
(4.6230)
Std Err of temperature
-0.1248
(0.3061)
0.5206
(0.3607)
-0.3588
(1.3495)
-0.4862
(3.2093)
Neighborhood index
0.0576***
(0.0013)
0.0583***
(0.0012)
0.1132***
(0.0056)
0.1507***
(0.0138)
Number of observations
19345
M cFadden's LRI
0.6769
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
APPENDIX B
Unused land
Estimate
Std Err
-3.8801*** (0.8209)
0.0345
(0.0934)
0.0414
(0.0781)
0.2703
(0.6201)
0.3852
(0.4356)
0.3911**
(0.1587)
-0.0552*
(0.0322)
0.1158
(0.1030)
-2.3025*** (0.8236)
-0.1096*** (0.0279)
-0.4653
(4.4423)
0.3350
(1.5463)
0.0793***
(0.0040)
52
Table A2.2. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Forestland, 1995-2000
Indep. Variable
Farmland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-3.1282*** (0.2178)
-2.8665*** (0.1752)
-4.8250*** (0.7760)
-6.0391
(4.1793)
Land productivity
0.0990***
(0.0117)
0.0545***
(0.0171)
0.0116
(0.0441)
0.0249
(0.1358)
County GDP
-0.0041
(0.0067)
0.0150
(0.0092)
-0.0010
(0.0491)
-0.0115
(0.0348)
Population
-0.0752
(0.0822)
-0.1991*
(0.1049)
0.2150
(0.4086)
-0.4762
(1.3089)
Agricultural investment
0.1755**
(0.0827)
0.1137
(0.0966)
0.2246
(1.0142)
0.3394
(4.7189)
Highway density
0.0393
(0.0459)
0.0786
(0.0605)
0.0395
(0.2035)
-0.2168
(0.5256)
Terrain slope
-0.0391*** (0.0077)
-0.0175*** (0.0063)
-0.1895*** (0.0505)
-0.1908
(0.1509)
Elevation
-0.2066*** (0.0483)
0.1420***
(0.0328)
-1.2181*** (0.1924)
-0.4573
(0.6115)
Precipitation
-0.4585*** (0.1338)
-1.1202*** (0.1378)
-0.6447
(0.5126)
-1.6371
(1.8932)
Temperature
0.0480***
(0.0085)
0.0391***
(0.0069)
0.0831***
(0.0300)
0.1561
(0.1177)
Std Err of precipitation
-1.1567**
(0.5049)
0.0428
(0.6458)
-2.6334
(1.9783)
-0.9844
(6.5859)
Std Err of temperature
1.0541***
(0.3198)
0.7215***
(0.2633)
1.3924
(1.1542)
-0.4434
(4.5842)
Neighborhood index
0.0411***
(0.0012)
0.0387***
(0.0012)
0.0813***
(0.0064)
0.1319***
(0.0160)
Number of observations
19488
M cFadden's LRI
0.6771
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-3.9343*** (0.7437)
0.1021
(0.0832)
-0.1897
(0.2619)
0.6480
(0.9809)
-2.1817**
(1.1069)
-0.8677*
(0.4527)
-0.1638*** (0.0411)
0.3046*
(0.1579)
-4.4378*** (0.7968)
0.0179
(0.0293)
0.1497
(3.0380)
2.6569**
(1.1504)
0.0536***
(0.0050)
53
Table A2.3. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Forestland, 1988-1995
Indep. Variable
Farmland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.4914*** (0.1422)
-2.8930*** (0.1445)
-2.2327
(1.8396)
-16.320**
(6.7621)
Land productivity
0.0004
(0.0060)
0.0610***
(0.0043)
0.0540
(0.2249)
1.0111
(0.6792)
County GDP
-0.0175
(0.0402)
-0.0804*** (0.0250)
-1.5031
(1.0247)
6.2719
(8.7911)
Population
-0.0616*** (0.0086)
-0.0203
(0.0126)
0.7417***
(0.0982)
0.2533
(0.5593)
Agricultural investment
0.0505*
(0.0302)
-0.1347*** (0.0363)
0.4098
(0.5779)
-0.1737
(3.4311)
Highway density
0.1655***
(0.0091)
0.1646***
(0.0174)
0.0018
(0.2189)
-0.2317
(0.1466)
Terrain slope
-0.0258
(0.0639)
0.0975
(0.0951)
0.0353
(0.8526)
-2.0554**
(0.9444)
Elevation
-0.1918*** (0.0501)
0.0719
(0.0978)
-0.1823
(0.7130)
-4.6428**
(2.0376)
Precipitation
-0.2156**
(0.1098)
-0.6717*** (0.1542)
-2.5938*
(1.5742)
2.8612
(5.6797)
Temperature
0.0306*
(0.0062)
0.0184***
(0.0049)
0.2139**
(0.0846)
0.4209
(0.3030)
Std Err of precipitation
-1.6763*** (0.3249)
0.1280
(0.4899)
-4.2647
(4.2123)
-36.9332*
(21.974)
Std Err of temperature
-0.1208
(0.2061)
0.6758***
(0.2598)
-2.5237
(3.1073)
26.6217**
(12.267)
Neighborhood index
0.0569***
(0.0008)
0.0582***
(0.0008)
0.1121***
(0.0104)
0.0858*
(0.0439)
Spatial parameter (ρ)
0.1113***
(0.0326)
Number of observations
19345
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-3.9696*** (1.3840)
-0.0607
(0.0865)
0.0989
(0.2123)
0.1812
(0.3189)
0.5776***
(0.1934)
0.3594***
(0.0835)
0.1893
(0.6361)
0.2502
(0.5595)
-0.9389
(1.8741)
-0.1563*** (0.0530)
-2.7104
(12.691)
0.8181
(3.0545)
0.0821***
(0.0066)
54
Table A2.4. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Forestland, 1995-2000
Indep. Variable
Farmland
Grassland
Water area
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.9879*** (0.1911)
-2.8763*** (0.1509)
-2.8032
(2.4545)
-4.7163
(13.748)
Land productivity
0.1003***
(0.0065)
0.0572***
(0.0044)
-0.0609
(0.2285)
-0.4921
(1.2736)
County GDP
0.0113
(0.0403)
-0.0220
(0.0249)
-1.1205
(1.4875)
5.2055
(4.7932)
Population
-0.0815*** (0.0071)
-0.2041*** (0.0128)
0.0915
(0.0948)
-0.9660**
(0.4532)
Agricultural investment
0.1801***
(0.0235)
0.1091***
(0.0295)
0.1969
(0.2802)
0.3395
(2.8244)
Highway density
0.0335***
(0.0056)
0.0761***
(0.0072)
0.0252
(0.0280)
-0.2228*** (0.0546)
Terrain slope
0.0226
(0.0617)
0.0713
(0.0922)
0.0083
(0.4085)
1.0567
(1.7127)
Elevation
-0.2216*** (0.0417)
0.1314**
(0.0571)
-0.963
(1.3877)
-0.9937
(3.6407)
Precipitation
-0.3677*** (0.0849)
-1.1385*** (0.1214)
-0.7238
(1.2673)
-2.0367
(6.1076)
Temperature
0.0371***
(0.0059)
0.0299***
(0.0051)
0.0764
(0.0804)
0.1456
(0.4834)
Std Err of precipitation
-1.1818*** (0.3182)
0.9039*
(0.4905)
-4.6062
(4.6128)
11.289
(30.353)
Std Err of temperature
0.9609***
(0.2185)
0.8341***
(0.1663)
-0.1402
(4.1137)
-7.5278
(21.492)
Neighborhood index
0.0401***
(0.0007)
0.0385***
(0.0007)
0.0745***
(0.0088)
0.1406**
(0.0628)
Spatial parameter (ρ)
0.0104**
(0.0400)
Number of observations
19488
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-3.0628*** (1.1302)
-0.2339
(0.1837)
0.0208
(0.3188)
0.6964**
(0.2779)
-2.3272**
(1.1077)
0.2435
(0.9153)
-4.3353
(3.3343)
-3.6362
(2.6858)
-4.3051**
(2.0884)
-0.1182*
(0.0643)
-0.3077
(7.1770)
4.2925**
(1.7857)
0.0439***
(0.0062)
55
Table A2.5. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Water Area, 1988-1995
Indep. Variable
Farmland
Forestland
Grassland
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-1.2987**
(0.6354)
-0.4612
(0.8319)
-1.9364**
(0.7971)
-5.0766**
(2.1037)
Land productivity
0.0178
(0.0344)
0.0748
(0.0545)
0.0569
(0.0557)
-0.1077
(0.1391)
County GDP
0.0038
(0.0473)
-0.0312
(0.0638)
0.0156
(0.0862)
0.0526
(0.0670)
Population
-0.2362
(0.3010)
0.2117
(0.3147)
-1.6273**
(0.6962)
0.5125*
(0.3031)
Agricultural investment
-0.8632
(0.5013)
0.2582
(0.8848)
0.2368
(0.9636)
-0.5197
(1.5840)
Highway density
0.5379
(0.2291)
0.1928
(0.2839)
-0.1738
(0.2443)
0.8766**
(0.3614)
Terrain slope
0.0400
(0.0708)
0.0480
(0.0555)
0.0241
(0.0251)
0.0197
(0.5225)
Elevation
-0.5889**
(0.2781)
-0.6459*
(0.3621)
0.0361
(0.1345)
0.0383
(0.7558)
Precipitation
0.2545
(0.6478)
-1.0663
(0.8095)
0.0482
(0.7722)
-0.6825
(2.4874)
Temperature
-0.0187
(0.0397)
0.0304
(0.0490)
0.0728**
(0.0333)
0.2135
(0.2106)
Std Err of precipitation
-0.5331
(1.9227)
-1.8934
(2.6294)
-3.3747
(3.5569)
-1.9733
(5.4332)
Std Err of temperature
-3.1317**
(1.3006)
-5.4057*** (1.6528)
-1.6622
(1.4569)
-3.9594
(4.2369)
Neighborhood index
0.0607***
(0.0044)
0.0703***
(0.0062)
0.0556***
(0.0043)
0.0863***
(0.0199)
Number of observations
1520
M cFadden's LRI
0.6046
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-1.9380*
(1.0214)
-0.0152
(0.1280)
-0.0827
(0.1738)
-0.8808
(1.0169)
-0.1456
(1.0513)
-0.0280
(0.3184)
0.0304
(0.0194)
-0.2351
(0.1673)
0.4337
(1.4136)
-0.0112
(0.0414)
-2.5342
(10.649)
-1.5564
(1.8052)
0.0622***
(0.0053)
56
Table A2.6. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Water Area, 1995-2000
Indep. Variable
Farmland
Forestland
Grassland
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-3.5146*** (0.8073)
-3.7134*** (1.2267)
-2.6763*** (0.7753)
-5.0281
(8.8008)
Land productivity
0.0510*
(0.0299)
-0.1498**
(0.0721)
-0.0780
(0.0640)
-0.0793
(0.3040)
County GDP
-0.0125
(0.0164)
-0.1913**
(0.0935)
-0.0766
(0.1137)
-0.0531
(1.0614)
Population
0.1826
(0.1634)
0.8162*
(0.4889)
0.0832
(0.8059)
-1.0301
(10.113)
Agricultural investment
0.1811
(0.5320)
0.6879
(0.8779)
-0.0209
(0.8614)
-2.9744
(49.987)
Highway density
0.3167**
(0.1523)
0.2871
(0.3881)
0.2984
(0.1967)
0.5073
(2.6891)
Terrain slope
0.0332
(0.0660)
-0.0219
(0.0720)
-0.0088
(0.0299)
0.2408
(0.4943)
Elevation
-0.3081
(0.2287)
-0.3472
(0.2199)
0.0328
(0.1515)
-2.1311
(3.7833)
Precipitation
-0.7302
(0.4833)
0.6039
(0.7174)
0.2418
(0.6534)
-4.0765
(7.4583)
Temperature
0.0416
(0.0346)
0.0286
(0.0432)
0.0272
(0.0345)
0.3731
(0.5964)
Std Err of precipitation
2.3506
(2.4991)
-5.3109*
(2.9318)
-4.1736
(4.2787)
-0.6482
(39.566)
Std Err of temperature
0.5533
(1.1182)
0.0807
(1.9814)
-0.3213
(0.9870)
-1.7576
(14.561)
Neighborhood index
0.0357***
(0.0037)
0.0563***
(0.0065)
0.0425***
(0.0044)
0.0660***
(0.0256)
Number of observations
1499
M cFadden's LRI
0.5461
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-0.4896
(0.9003)
-0.0385
(0.0663)
0.0635*
(0.0381)
-1.1591*
(0.5957)
0.4033
(0.6715)
0.0319
(0.3327)
-0.0215
(0.0273)
-0.1232
(0.1482)
-0.2975
(0.8468)
-0.0782
(0.0363)
2.3921
(4.6187)
-2.5544**
(1.2470)
0.0358***
(0.0041)
57
Table A2.7. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Water Area, 1988-1995
Indep. Variable
Farmland
Forestland
Grassland
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-1.8994*** (0.6160)
-1.1767
(1.0315)
-2.2193*** (0.8342)
-0.6541
(5.2873)
Land productivity
-0.0494
(0.0591)
0.0171
(0.0536)
0.0508**
(0.0204)
-0.2956
(0.5526)
County GDP
0.6754**
(0.2655)
0.6877*
(0.3978)
-0.0283
(0.1480)
-1.6757
(2.1664)
Population
-0.2335*** (0.0319)
0.1949***
(0.0690)
-1.6255*** (0.0760)
0.4767***
(0.2184)
Agricultural investment
-1.1074
(0.2532)
-0.2126
(0.4021)
0.1824
(0.2889)
-0.676**
(0.3372)
Highway density
0.5194***
(0.0409)
0.2285***
(0.0745)
-0.1432
(0.1364)
0.8811***
(0.0951)
Terrain slope
0.0825
(0.2287)
0.0103
(0.2487)
0.6613
(1.2947)
-0.1976
(0.4324)
Elevation
-0.4621
(0.5539)
0.0522
(0.9268)
-0.1363
(0.8458)
0.4193
(1.0127)
Precipitation
0.8679
(0.6391)
-0.7604
(1.0214)
1.3498
(0.9425)
-1.3304
(3.9337)
Temperature
-0.0860**
(0.0384)
0.0052
(0.0526)
0.0374
(0.0343)
0.0846
(0.3042)
Std Err of precipitation
1.1571
(1.7994)
-1.0477
(3.4869)
-7.1965
(5.4740)
-2.1646
(11.204)
Std Err of temperature
-1.1016
(1.2709)
-2.0640
(2.3261)
0.2087
(1.3601)
-3.3985
(7.9236)
Neighborhood index
0.0607***
(0.0042)
0.0687***
(0.0085)
0.0541***
(0.0043)
0.0706**
(0.0291)
Spatial parameter (ρ)
0.4308***
(0.0971)
Number of observations
1520
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-2.0830**
(0.9170)
-0.0195
(0.0235)
-0.1322
(0.2058)
-0.6868**
(0.2014)
-0.1408
(0.4422)
0.0288
(0.2602)
-0.6756
(1.4122)
-0.0677
(1.4673)
1.4839
(1.5847)
-0.0402
(0.0508)
-2.7134
(11.342)
0.0644
(1.6562)
0.0673***
(0.0067)
58
Table A2.8. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Water Area, 1995-2000
Indep. Variable
Farmland
Forestland
Grassland
Urban area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-1.8363*** (0.7096)
-0.5158
(1.7580)
-1.3938*
(0.8229)
-1.1221
(11.327)
Land productivity
0.0786
(0.0589)
-0.2172**
(0.1016)
-0.0619**
(0.0264)
-0.4175
(0.4205)
County GDP
0.2139
(0.2277)
-0.1685
(0.4783)
-0.2219
(0.1493)
5.9953
(7.2205)
Population
0.1964***
(0.0208)
0.8974***
(0.1059)
0.1628**
(0.0772)
-0.8920*** (0.3459)
Agricultural investment
0.1339
(0.1366)
0.6698**
(0.3189)
-0.1140
(0.1779)
-3.3167**
(1.4755)
Highway density
0.3284***
(0.0111)
0.5044***
(0.1725)
0.2482*
(0.1433)
1.9945
(1.4160)
Terrain slope
-0.0392
(0.1098)
-0.5313
(0.5956)
0.4134
(0.8284)
-9.6580
(7.3380)
Elevation
-0.3343
(0.2929)
-0.8531
(0.6399)
-0.5860
(0.8721)
-1.0027
(49.686)
Precipitation
-0.2450
(0.4097)
-0.5815
(0.8611)
0.3456
(0.7523)
3.8893
(7.1175)
Temperature
-0.0222
(0.0308)
-0.0047
(0.0544)
0.0007
(0.0312)
-0.3088
(0.6331)
Std Err of precipitation
0.8176
(1.8157)
-2.8022
(3.2156)
-5.9253
(4.5718)
16.355
(46.403)
Std Err of temperature
-0.2159
(0.9671)
-1.3158
(2.5181)
0.0060
(0.8827)
2.8721
(17.650)
Neighborhood index
0.0336***
(0.0027)
0.0506***
(0.0085)
0.0373***
(0.0036)
0.0235
(0.0355)
Spatial parameter (ρ)
0.4108***
(0.0864)
Number of observations
1499
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Unused land
Estimate
Std Err
-0.7791
(0.6841)
-0.0344
(0.0212)
0.1879
(0.1170)
-1.1285*** (0.1123)
0.6434*
(0.3804)
0.0578
(0.0385)
0.4459
(0.8933)
-0.4297
(0.4684)
-1.2845
(0.8413)
-0.0518
(0.0306)
9.6359
(4.6155)
-1.9914
(1.0886)
0.0354***
(0.0030)
59
Table A2.9. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Unused Land, 1988-1995
Indep. Variable
Farmland
Forestland
Grassland
Water area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-2.9605*** (0.6643)
-7.6077*** (0.8299)
-5.1937*** (0.2789)
-6.9052*** (0.7162)
Land productivity
0.0889*
(0.0497)
-0.0770
(0.1078)
0.0118
(0.0479)
0.1109
(0.0729)
County GDP
0.3721***
(0.1439)
-0.1357
(0.3696)
0.2932***
(0.0720)
0.3161*
(0.1675)
Population
-0.3381
(0.7308)
2.0092*
(1.0686)
-0.7116*
(0.4122)
0.0264
(0.9001)
Agricultural investment
-2.4682
(2.2017)
-0.0468
(0.7565)
-1.686***
(0.2841)
-1.8844**
(0.8420)
Highway density
0.4908***
(0.1322)
0.2492
(0.2135)
-0.3221*** (0.0912)
0.4114*
(0.2484)
Terrain slope
-0.1487
(0.1185)
-0.0228
(0.0357)
-0.0079
(0.0094)
-0.0187
(0.0239)
Elevation
-0.9107*** (0.2407)
0.2993**
(0.1354)
0.2632***
(0.0491)
-0.2121*
(0.1147)
Precipitation
2.3108**
(0.9521)
2.9074***
(1.0460)
2.7848***
(0.4213)
3.9858***
(1.0558)
Temperature
-0.1522*** (0.0318)
0.0641**
(0.0322)
0.0424***
(0.0117)
-0.1531*** (0.0293)
Std Err of precipitation
0.4155
(5.8175)
1.4269
(5.6772)
-2.0200
(2.7633)
0.9080
(7.8870)
Std Err of temperature
-0.9815
(1.2870)
1.8069
(1.5764)
4.2513***
(0.4904)
5.6654***
(1.3302)
Neighborhood index
0.0669***
(0.0038)
0.0917***
(0.0047)
0.0483***
(0.0012)
0.0880***
(0.0046)
Number of observations
13748
M cFadden's LRI
0.7996
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Urban area
Estimate
Std Err
-5.2945
(207.12)
0.6917
(7.3289)
0.2265
(9.8671)
-2.5527
(173.50)
0.3002
(157.35)
-1.3511
(81.250)
-2.6121
(126.67)
-0.8593
(44.950)
-3.0495
(330.89)
-0.0963
(5.4338)
-0.7762
(1191.0)
-1.6975
(595.47)
0.1133
(0.6806)
60
Table A2.10. Coefficient Estimates for the Standard M ultinomial Logit M odel of Land-use Change on Unused Land, 1995-2000
Indep. Variable
Farmland
Forestland
Grassland
Water area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-4.8764*** (0.6026)
-4.8348*** (0.8487)
-3.7899*** (0.2968)
-5.1084*** (0.7763)
Land productivity
0.2270***
(0.0407)
0.1060
(0.1063)
0.0917*
(0.0488)
0.0971
(0.0969)
County GDP
-0.0274
(0.0691)
-0.0516
(0.2770)
-0.0033
(0.0976)
-0.0500
(0.2248)
Population
0.6492
(0.5992)
0.5469
(1.4022)
-0.3835
(0.4998)
0.1833
(1.0677)
Agricultural investment
-0.9822
(0.9698)
0.8982
(0.8526)
-1.0307*** (0.3874)
0.7685
(0.7521)
Highway density
0.6207***
(0.084)
0.5508***
(0.1871)
0.3246***
(0.0734)
0.2749
(0.2886)
Terrain slope
0.0499
(0.0659)
0.0540
(0.0356)
-0.0133
(0.0083)
0.0050
(0.0227)
Elevation
-1.3933*** (0.2063)
-0.1362
(0.1450)
0.1523**
(0.0615)
-0.4027*** (0.1527)
Precipitation
1.0789
(0.7369)
2.9483***
(0.8192)
0.2822
(0.4103)
2.3348***
(0.9070)
Temperature
-0.1027*** (0.0256)
-0.0449
(0.0338)
-0.0430*** (0.0127)
-0.1669*** (0.0325)
Std Err of precipitation
7.2734**
(3.0348)
0.0589
(3.2958)
10.155***
(2.0429)
3.7363
(5.4435)
Std Err of temperature
2.5676***
(0.9076)
-2.0130
(1.4603)
0.3636
(0.3312)
1.4047
(0.9426)
Neighborhood index
0.0365***
(0.0038)
0.0749***
(0.0053)
0.0367***
(0.0014)
0.0627***
(0.0049)
Number of observations
13276
M cFadden's LRI
0.8246
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Urban area
Estimate
Std Err
-2.1247
(66.415)
-2.4456
(25.816)
-0.7431
(6.5123)
-2.4836
(53.576)
5.6398
(9.5645)
0.1292
(7.7573)
-0.4287
(8.5267)
-4.6396
(21.063)
-0.2501
(94.280)
-0.4150
(1.9989)
0.2473
(239.16)
0.2657
(100.50)
0.2189
(0.3466)
61
Table A2.11. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Unused Land, 1988-1995
Indep. Variable
Farmland
Forestland
Grassland
Water area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
-1.1580*
(0.6435)
-7.9209*** (1.1678)
-3.6298*** (0.2195)
-6.6046*** (0.9006)
Land productivity
-0.1035
(0.1709)
-0.1823**
(0.0741)
0.0152***
(0.0057)
0.1399***
(0.0294)
County GDP
1.6850***
(0.2944)
0.8448***
(0.1946)
0.2583***
(0.0301)
0.5824***
(0.1362)
Population
-0.2937*** (0.0375)
1.9131***
(0.1477)
-0.6797*** (0.0328)
-0.1433*
(0.0777)
Agricultural investment
-2.6681*** (0.0874)
-0.2365
(0.1974)
-1.5420*** (0.0743)
-2.0739*** (0.1480)
Highway density
0.5411**
(0.2144)
-0.0435
(0.5343)
-0.2848*** (0.0884)
0.6723***
(0.1384)
Terrain slope
1.7388**
(0.8552)
4.3721***
(1.3380)
1.2258***
(0.2737)
1.0015
(0.7198)
Elevation
-1.7953
(3.2603)
-0.3226
(1.5399)
-0.2791
(0.2395)
-5.5762*** (1.6775)
Precipitation
-0.5085
(1.0432)
-2.0745*
(1.1036)
0.7017**
(0.3022)
-0.0357
(1.0667)
Temperature
-0.1062*** (0.0319)
0.1725***
(0.0413)
0.0426***
(0.0066)
-0.0954**
(0.0423)
Std Err of precipitation
1.4730
(5.6052)
15.299**
(6.2364)
-4.9435*** (1.4370)
11.046
(8.1613)
Std Err of temperature
-5.4274*** (1.3844)
-0.4240
(2.0870)
2.5256***
(0.2920)
8.0209***
(1.6261)
Neighborhood index
0.0623***
(0.0043)
0.1065***
(0.0073)
0.0463***
(0.0006)
0.0845***
(0.0054)
Spatial parameter (ρ)
0.2384***
(0.0288)
Number of observations
13748
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Urban area
Estimate
Std Err
-22.558
(80.246)
-500.30*** (133.80)
-18.673
(17.437)
5.1809
(5.0428)
-113.84**
(52.227)
-5.0450
(4.1769)
4.1919
(114.21)
76.936
(59.914)
-667.13*** (168.07)
-0.5171
(2.2688)
2195.1***
(585.71)
374.50*
(211.82)
-0.6196*
(0.3189)
62
Table A2.12. Coefficient Estimates for the Spatial M ultinomial Logit M odel of Land-use Change on Unused Land, 1995-2000
Indep. Variable
Farmland
Forestland
Grassland
Water area
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Estimate
Std Err
Intercept
0.7936
(0.5268)
0.7583
(0.8980)
0.0516
(0.1835)
-0.4944
(0.8034)
Land productivity
0.5044***
(0.0840)
0.0781***
(0.0237)
0.1144***
(0.0044)
0.1153***
(0.0196)
County GDP
1.6538***
(0.2510)
0.6779***
(0.1273)
-0.1979*** (0.0313)
-0.1728
(0.1456)
Population
0.5643***
(0.0253)
0.5666***
(0.0943)
-0.3894*** (0.0244)
0.0930
(0.1113)
Agricultural investment
-1.2783*** (0.0592)
0.6952***
(0.1269)
-1.1846*** (0.0321)
0.2614
(0.3409)
Highway density
0.7369***
(0.0400)
0.6680***
(0.1924)
0.2841***
(0.0340)
0.4318***
(0.1241)
Terrain slope
-0.8374*** (0.2840)
-0.9899
(0.9374)
0.8731***
(0.2207)
-0.5967
(0.6022)
Elevation
-6.3319*** (1.4206)
0.0722
(0.9794)
-0.1784
(0.2544)
-1.1809
(0.8196)
Precipitation
-4.0402*** (0.3995)
-2.8814*** (0.6699)
-2.6201*** (0.2151)
-0.6586
(0.8663)
Temperature
0.1626***
(0.0191)
0.0715**
(0.0343)
-0.0361*** (0.0063)
-0.1491*** (0.0390)
Std Err of precipitation
2.9214
(1.8005)
3.1733
(2.2808)
-0.4682
(1.0251)
-2.9577
(4.6923)
Std Err of temperature
-2.8901*** (0.8245)
-3.9449**
(1.6225)
0.0504
(0.1563)
3.0074***
(1.1234)
Neighborhood index
0.0364***
(0.0021)
0.0631***
(0.0055)
0.0303***
(0.0006)
0.0624***
(0.0036)
Spatial parameter (ρ)
0.8865***
(0.0315)
Number of observations
13276
Note: *, **, and *** indicate statistical significance at 10, 5, and 1% levels, respectively.
Urban area
Estimate
Std Err
27.642
(71.712)
-25.291
(19.476)
-8.6139
(28.553)
48.085
(35.433)
1.4877
(8.7185)
4.0688
(5.0529)
-56.152
(42.321)
2.0491
(11.939)
-3.256
(100.47)
-1.6407
(2.4767)
-103.76
(159.85)
14.897
(70.029)
0.6825
(0.4681)
63
64
CHAPTER 3
AN EMPIRICAL ECONOMIC ANALYSIS OF LAND-USE CHANGE
AND SOIL CARBON SEQUESTRATION IN CHINA
MAN LI
65
ABSTRACT
This essay presents an empirical analysis to examine the relationship between
land-use change and soil carbon sequestration in China. The analysis compiles highquality, national-scale land use data with a spatial resolution of 30 by 30 meters and soil
profile data (10 by 10 kilometers). A statistical soil organic carbon (SOC) density model
and an econometric land-use change model are developed to link the socioeconomic
factors with the SOC density. The approach captures spatial autocorrelation and spatial
heterogeneity simultaneously and can be applied to a large region. Results indicate that
the SOC density is highest in forest and grass lands and lowest in unused land in China.
GDP growth leads to farmland and grassland loss, deforestation, and idleness, which
accelerates soil carbon runoff. The models are integrated to evaluate the welfare effects
of China‟s green growth policy, which targets resource reservation and environment
protection in conjunction with soil carbon sequestration. Results indicate that the policy
could generate up to 0.7-1.1 million Mg SOC and 22.2-37.4 million CNY welfare losses
annually throughout the simulation period (2001-2050). The marginal welfare loss is
approximately ¥15.3/Mg (equivalent to $2.25/Mg) for sequestering about 1 million Mg
SOC per year. A comparison of the results reveals that soil-based carbon sequestration,
oriented by the green growth policy, merits consideration.
66
INTRODUCTION
Carbon dioxide (CO2) emissions are going up at a rapid speed in China.
Increased energy demand, driven by fast economic development and unprecedented
urbanization, makes it a limited strategy for China to rely on energy-based abatement
alone. As a supplement, biological carbon sequestration has attracted more attention
because of its environmental benefits for soil conservation and sustainable economic
development. Biological sequestration involves managing land in ways that enhance the
natural absorption of atmospheric carbon by soil and vegetation. Economic efficiency is a
major criterion for evaluating the feasibility of alternative carbon sequestration strategies.
Many American and European researchers have addressed this issue by estimating the
sequestration costs in the United States and European countries. Their primary interests
are forest sequestration costs (Adams et al. 1993; Alig et al. 1997; Lubowski et al. 2006;
Parks and Hardie 1995; Plantinga et al. 1999; Stavins 1999) and economic potential for
agricultural soil sequestration (Pautsch et al. 2001; Antle and Diagana 2003; Capalbo et
al. 2004; Feng et al. 2006; Antle et al. 2007). It shows that cost estimation of
conservation tillage is sensitive to the choice of baseline and the spatial heterogeneity of
the area. Richards and Stokes (2004) reviewed the literature on this subject. They found
that afforestation in the United States could sequester 250 to 500 million megagrams of
carbon (Mg C) per year at a price ranging 10-150 USD per Mg C, whereas conservation
tillage would generate 0.25 to 6.2 million Mg C in soil per year by spending 12-270 USD
per Mg C.
With the total land area of 946.8 million hectares, China, like the United States
and Canada, has a large potential for soil carbon sequestration. Nevertheless China has
witnessed a decreased storage of soil organic carbon (especially in grassland) resulting
from dramatic land use change over the past decades. According to the first and second
national soil surveys of China, the storage of soil organic carbon (SOC) was estimated to
drop from 93 petagrams of carbon (Pg C) in the 1960s to 92 Pg C in the 1980s (Wang et
al. 2003). Specially, extensive agricultural development and desert expansion reduced the
SOC density by 10-20 kg/m2 in the alpine meadow of the Southeast Tibet Plateau and by
67
2-3 kg/m2 in the grassland of the Mid-east Inner Mongolia. It is of practical importance to
estimate the spatial distribution and dynamic changes of soil carbon storage in China.
Many Chinese scientists have made efforts to obtain such knowledge (Fang et al. 2001;
Tang et al. 2006; Wang et al. 2003; Wang et al. 2004; Wu et al. 2003; Yang et al. 2007;
Zhang et al. 2006). Huang and Sun (2006) surveyed the changes in topsoil carbon of
croplands in China over the last two decades by selecting 132 representative articles from
literature databases published since 1993. Canadian and Chinese researchers also
implemented a four-year (2002-2006) project of carbon sequestration in China‟s forest
ecosystems and their achievements were published in a special issue of Journal of
Environmental Management (2007). These studies have two limits in common: 1) the
focus is mainly on scientific analysis without taking socioeconomic factors into account,
and 2) the estimation of soil carbon typically relies on site-specific process models1 that
were developed based on field-level inputs and hence are restricted to local analysis in a
relatively small region.
The objectives of this essay are to estimate the association between land-use
change and SOC density in China and to evaluate the welfare effects of the green growth
policy that targets co-benefits of soil carbon sequestration and environmentally
sustainable development. For this purpose, we compiled a unique, national-scale dataset
that includes high quality land use data and soil profile data. Using 10-kilometer-gird (10km-gird) as the analysis unit, we develop a statistical model to explore the association of
SOC density with six major land uses, i.e., farmland, forestland, grassland, water area,
urban area, and unused land. Besides land use dummy, the right-hand-side covariates
include soil profile and climate variables. This approach can be applied to a large region
and thus overcomes the limitation of a process model that typically works for the fieldlevel study. We adopt a version of multinomial logit model for land-use change from
1995 to 2000, as developed and discussed in the first essay (Chapter 2), to analyze the
effects of socioeconomic factors on land conversion among six uses. Socioeconomic
variables are measured at county level, including county gross domestic product (GDP),
population, public agricultural investment, and highway density. By combining two
68
models, we are able to use the “logsum” formula to estimate welfare changes for different
GDP growth rates (Small and Rosen 1981).
This essay contributes to the literature in two aspects. First, it is the first
economic research on soil carbon sequestration in China, whereas previous studies are
typically conducted by scientists. Second, it applies the “logsum” formula to evaluate the
welfare effects of policy-oriented land conversion. The “logsum” is a measure of
consumer surplus in the context of (multinomial) logit models and has been used in some
fields, such as transport demand, recreation demand, and residential location (Mansfield
et al. 2008; Phaneuf et al. 2008). But to the best of our knowledge, there have not been
such applications in the land use literature. Econometrically, this essay applies
geographically weighted regress (GWR) technique to a spatial autoregressive (SAR)
model to correct for spatial autocorrelation and spatial heterogeineity simultaneously,
which is a novel application of spatial econometrics in empirical study.
The remainder of this essay is arranged as follows. Section 2 discusses the SOC
density model. Section 3 describes data. Section 4 reports the estimation and simulation
results. Section 5 generates a discussion on green growth, carbon sequestration, and
welfare losses. The final section concludes.
69
THE SOC DENSITY MODEL
The dynamics of SOC flow are a complex process, where SOC storage is
determined by the balance of carbon inputs from plant production and outputs through a
decomposition process (Jobbágy and Jackson 2000; Parton et al. 1993; Schlesinger 1977)
and soil temperature, moisture, and texture jointly control the decomposition rates of
SOC in various carbon pools (Parton et al. 1993). Interactions among these factors
generally demonstrate a complicated, nonlinear pattern. For example, the effects of soil
temperature and soil moisture on the decomposition rates exhibit an inverted-U shape
with a heavy left-tail. The decomposition rates of SOC in the active pool tend to increase
with sand content and the decomposition rates in the slow carbon pool tends to decrease
with clay content. SOC density is negatively correlated with soil bulk density (Wang et al.
2004; Wu et al. 2003; Yang et al. 2007).
It is challenging to apply detailed site-specific process models for regional
analysis with field-level inputs of soil profile and vegetation. To overcome this challenge,
we develop a statistical model that relies on relatively flexible data and hence can be
tailored for specific use and easily be applied to a large region. Of primary interest is the
association of SOC density with six major land uses. The covariates2 specified in this
model include soil profile and climate factors, which are discussed in details in the data
section. Equation (3.1) presents the general formula of the model.
(3.1)
y = Xθ + ε ,
where the bold type denote a vector or a matrix, dependent variable y is the logarithm of
SOC density; X represents independent variables, i.e., land use dummy, soil profile, and
climate variables; θ is the coefficient of X ; ε denotes error term. We adopt a quadratic
polynomial functional form of soil profile and climate factors in the analysis, which
allows capturing the potential nonlinear relationships within and between these variables.
There are six land use groups: farmland, forestland, grassland, water area, urban area, and
unused land. Soil profile variables include PH value, bulk density, and soil loam and sand
contents.3 Climate variables include mean annual precipitation and mean annual
70
temperature. Previous study explained 84% of the variations in SOC storage in China
using similar soil profile and climate variables (Yang et al. 2007). We refer to equation
(3.1) as OLS model since it can be estimated using ordinary least squares (OLS)
regression.
When applying contiguous geographic data for the empirical analysis, OLS
regression is inappropriate because of potentially spatial heterogeneity (spatial variation
in parameters) and spatial autocorrelation (spatially correlated error term and/or spatially
correlated dependent variable). The costs of not correcting for these issues are
inconsistent/biased estimates if there is spatially interdependence or spatial heterogeneity.
But in practice it is technically infeasible to distinguish between them. In this study, the
OLS model is able to capture potential endogeneity resulting from spatial autocorrelation
in the dependent variable because the disaggregated data on soil and climate are
generated by interpolating the original point data into a surface respectively using
Kriging algorithm (Kravchenko and Bullock 1999) and thin plate smoothing spline
method (Hartkamp et al. 1999). These techniques have already taken spatial effects into
account when estimating or retrieving the values of other locations during the
interpolation process.
To correct for the remaining problems, we extend OSL regression of equation
(3.1) to a spatial autoregressive (SAR) model which allows for spatial error
autocorrelation; meanwhile, we adopt geographically weighted regress (GWR) technique
to capture spatial heterogeneity in coefficients (Fotheringham et al. 1998). The model is
rewritten as
(3.2)
y = Xθ  ui , vi  + ε,
ε =  Wε + μ,
where  ui , vi  denotes the coordinates of the i th point in space, θ  ui , vi  is a realization
of the continuous function θ  u, v  at point i ,  is the spatial autoregressive coefficient,
W is a row-standardized n  n matrix with wii  0 and

n
j 1
wij  1 for each i , μ is
71
heteroscedastic noise such that E  μμ    2M  ui , vi 
1
, and M  ui , vi  is an n  n
diagonal matrix. Hence the second moment of error ε is expressed as
(3.3)
E  εε    2  I   W  M  ui , vi 
1
1
 I   W
1
,
which is equivalent to its variance-covariance matrix.
We refer to equations (3.2) and (3.3) as the spatial model. There are two weight
matrices, W and M  ui , vi  , used for SAR and GWR, respectively. We assume a
substantially identical weighting scheme in both matrices, where each non-zero entry is
specified as a Gaussian function of geographical distance from location j to location i ,
as in
(3.4)
wij  exp   dij2 h2 

n
j 1
exp   dij2 h2  ,
 i, j  1,
, n, and i  j
and
(3.5)
m jj  ui , vi   exp   dij2 h2  ,
 i, j  1,
,n .
In equations (3.4) and (3.5), d ij measures the Euclidean distance between points i and j ,
and h is referred to as the bandwidth. One difficulty with spatial regression is that the
estimated parameters are, in part, functions of the weighting function. As the bandwidth
h tends to infinity, the weighting function exp   dij2 h2  tend to one for all pairs of
points so that wij   n  1
1
 j  i and m jj  ui , vi   1  i, j . Equivalently, the weights
wij and m jj  ui , vi  becomes uniform for every point j no matter how far it is from
location i , and GWR becomes equivalent to SAR. Conversely, as h becomes smaller, the
parameter will increasingly depend on observations in close proximity to i . In particular,
the weighting function exp   dij2 h2  tends to zero when the distance d ij exceeds
approximately 2.15 times as long as the bandwidth h . The problem hence becomes how
to select an appropriate bandwidth or decay function in regression. In this study we
choose h on a criterion of minimum Predicted Residual Error Sum of Squares (PRESS),
where the fitted value with the point i omitted from the calibration process.
72
The essential idea of GWR is that for each point i there is a bump of influence
around i corresponding to the weighting function so that sampled observations near to i
have more influence in the estimation of the parameters of i than do sampled
observations farther away. We perform weighted least squares regression for each point i
in a SAR model. Equation (3.6) demonstrates the expression of the theoretical coefficient
estimates.
1
(3.6) θˆ  ui , vi    X  I   W M  ui , vi  I   W  X X  I   W  M  ui , vi  I   W  y .
SAR models are typically estimated by the maximum likelihood (ML) method, where the
likelihood function corresponds to the normal distribution. A practical difficulty with the
ML method in SAR models is that the estimation of  entails significant computational
complexities. In this essay we adopt a generalized method of moments (GMM) estimator
that is computationally simple irrespective of the sample size (Kelejian and Prucha 1999).
Besides, we conduct Lagrange Multiplier (LM) test for every GWR as shown in equation
(3.7).
(3.7)
LM   ui , vi   nμˆ   ui , vi  Wμˆ  ui , vi  μˆ   ui , vi  μˆ  ui , vi 
2
tr  WW  WW  ,
where μˆ  ui , vi  is a GWR residuals based on estimation under the restricted model, i.e.,
  0 . The test statistic is asymptotically distributed as  1 (Burridge 1980).
73
DATA
Our study covers Mainland China. Most data used in this essay were provided
by the Chinese Academy of Sciences (CAS) including soil profile, climate, land use,
terrain, and socioeconomic data. They are measured at a scale of 10 by 10 square
kilometers, except for socioeconomic data, which are measured at county level. Table 3.1
provides a detailed summary of the data.
Table 3.1. Summary Statistics of Explanatory Variables
Variable
Measurement Unit
10-km-gird level
SOC density
N
Mean
Std. Dev.
Minimum
Maximum
kg/C m2
93,802
6.242
5.736
0.274
82.957
Soil PH value
N/A
93,802
6.060
1.835
0.000
9.000
Bulk density
g/cm3
93,802
1.340
0.149
1.013
2.544
Soil loam content
percent
93,802
24.40
9.62
0.00
54.00
Soil sand content
percent
93,802
56.06
17.71
19.00
100.00
Precipitation, long-term
1000 mm
93,802
0.579
0.496
0.009
2.498
Temperature, long-term
degree Celsius
93,802
5.887
8.127
-20.900
26.000
Land productivity
Terrain slope
Elevation
Precipitation, 1996-2000
Std. of precipitation, 1996-2000
Temperature, 1996-2000
Std. of temperature, 1996-2000
g/ha.
degree
km
1000 mm
1000 mm
degree Celsius
degree Celsius
93,902
94,662
94,612
94,173
94,173
94,173
94,173
1.413
3.555
1.837
0.478
0.081
6.677
0.599
2.632
5.010
1.742
0.436
0.067
8.045
0.153
0.000
0.000
-0.153
0.006
0.002
-17.000
0.239
14.168
72.790
7.040
1.824
0.368
31.620
1.693
m/10000 ha.
billion CNY
billion CNY
million people
million people
million CNY
million CNY
2,331
2,247
2,251
2,332
2,333
2,143
2,140
1.022
2.593
3.956
0.510
0.529
0.077
0.096
3.794
6.518
11.121
0.499
0.514
0.423
0.526
0.000
0.021
0.041
0.006
0.006
0.000
0.000
155.708
202.418
364.877
10.616
10.817
13.653
17.057
county level
highway
GDP, 1996
GDP, 2000
Population, 1996
Population, 2000
Agricultural investment, 1999
Agricultural investment, 2000
Data on soil profile were generated from a geographical information system
(GIS) database and includes cross-sectional data of SOC density, PH value, bulk density,
soil loam and sand content. They were initially collected by a special nationwide research
and documentation project (the Second National Soil Survey of China) organized by the
74
State Council and run by a consortium of universities, research institutes and soils
extension centers. CAS interpolated the soil information into surface data using Kriging
algorithm, to get more disaggregated information for each pixel. Data on land-use type,
terrain, climate, and socioeconomic variables have been discussed in the first essay
(Chapter 2).
75
RESULTS
We conduct cross-sectional estimation for the SOC density model (equations
(3.2) - (3.3)) and the land-use change model (equations (2.2) - (2.3)), respectively, at the
unit of 10-km-grid scale. Estimation results of two models are reported and discussed
below
SOC Density Model
Figure 3.1. Histogram of R-square for all GWR‟s in the SOC density model.
Under the criterion of minimum Predicted Residual Error Sum of Squares
(PRESS), the bandwidth h is estimated to be 75 km. The essential idea behind the
bandwidth is that for each location i there is a circle centered at i with a radius of 161.25
km ( 161.25 km  75 km  2.15 ). Within the circle points around i have “bump of
influence” on i ; beyond the circle the influence of points are negligible. We perform
GWR for each point i in the context of SAR model. GWR reports complete coefficient
76
estimates for 48830 observations out of 93820 in total.4 So the attributes of estimated
parameters are discussed via their statistical distributions rather than any particular point
estimates.
1.0
coefficient
0.5
0
-0.5
-1.0
farmland
forest
grass
water
urban
unused
landtype
Figure 3.2. Box plot of land-use dummy estimators for all GWR‟s in the SOC density model.
The SOC density model performs well. Figure 3.1 illustrates the distribution of
R2, with the mean value equal to 0.633. For each SAR model, there is convincing
evidence that error term is correlated over space (P-value<<0.001). The spatial
autoregressive parameter (  ) is estimated to be 0.999 uniformly in all models. We
remove the intercept from the regression to avoid collinearity caused by the land-use
dummy. Thus the reference is referred to as the expected mean value of logarithm of
SOC density for the pooled sample, which contains all observations of the six land-use
categories. The absolute magnitude of coefficient of land-use dummy measures the
difference in the expected mean of logarithm of SOC density for each separate land-use
category relative to the reference. The P-value associated with the land-use dummy
77
variable ranges between 0.000 and 0.100. The average of the P-value is 0.032, presenting
credible evidence for the statistically significant association of the logarithm of SOC
density with land uses. We also report box plots of land-use dummy estimates by uses
(Figure 3.2). It demonstrates large spatial variations in the regression results. Among
estimates of six land-use dummies, forestland has the highest average (0.0068) and is
followed by grassland (0.0030). By contrast, the average estimates of four remaining uses
are found to be negative. In particular, unused land has the lowest mean (-0.0092).
Like land-use dummy estimators, estimates of other covariates exhibit spatial
nonstationarity. We do not generate a discussion on these covariates because they are not
of our primary interest in the essay. Estimation results of these variables are available
upon request.
Land Use Change Model
For each initial land use, we employ ML method to estimate multinomial logit
models on land use changes from 1995 to 2000. There are five initial uses (farmland,
forestland, grassland, water area, and unused land) and six final uses (farmland,
forestland, grassland, water area, urban area, and unused land). So there are five separate
models in total. These models perform well, where pseudo R2 (McFadden's likelihood
ratio index) ranges between 0.546 and 0.825. We report the estimation results of land-use
change on farmland, grassland, forestland, water area, and unused land in Tables 2.3b,
2.5b, A2.2, A2.6, and A2.10 (Chapter 2). Estimates and standard errors of parameters of
equation (2.3) are reported in each column of these tables by land-use choice.
It shows that the sign, magnitude, and statistical significance of estimates are in
line with the economic interpretation. For example, all transition-specific constants have
negative estimates and almost all of them are statistically different from zero at the 1%
level, indicating that conversion cost deters land conversion on initial use. Likewise, the
estimates of land productivity are negatively significant in all columns of Table 2.3b and
positively significant in the first columns of Tables 2.5b, A2.2, and A2.10, implying that
a land patch with higher crop yield potential is more likely to be used for farming. We
78
also found that the odds of land conversion are associated with climate, including mean
annual rainfall, mean annual temperature, and their temporal variations.
The results provide strong evidence for the association between the odds of land
use change and socioeconomic factors, i.e., county GDP, population, public agricultural
investment, and highway density. The sign and statistical significance of estimates are
consistent in land conversion on different uses. As is shown, GDP estimates are reported
statistically positive in the utility equations of unused land in Table 2.3b (land-use change
on farmland) and statistically negative in the utility equations of farmland in Table 2.5b
(land-use change on grassland), implying that land is less likely to be used for cultivation
in a county with higher level of GDP. With higher GDP, the demand for residential
development and industrial and commercial uses increases. Farming is generally a lowpaying job in China, so farmers are willing to be engaged in other higher-returned
activities rather than farming. Conversely, farmers are more likely to farm in a county
with low GDP for the lack of high-paying jobs.
Estimates of population are statistically negative in the utility equations of
grassland in Tables 2.3b and A2.2 (land-use change on farmland and forestland) and are
statistically positive in the utility equations of farmland and forestland in Table 2.5b
(land-use change on grassland), implying that cultivation and afforestation are more
likely to take place than pasturing on a patch of land that possesses larger population. In
addition, a patch of grassland with smaller population is more likely to be converted to
unused land. The effects of highway density on land conversion are very similar to those
of population. Highway density measures freights of conveying agricultural and forest
products. Dense highway tends to lower transport costs, which decreases the probabilities
of farmland conversions and deforestation. Public agricultural investment increases the
probabilities of agricultural use (farmland and grassland), because investment in
agriculture infrastructure such as irrigation can to improve agricultural productivity in the
long run and hence increase the value of agricultural land. Consequently, the opportunity
cost of converting land out of agricultural use is raised.
79
GREEN GROWTH, CARBON SEQUESTRATION, AND WELFARE LOSS
It is impressive that China has been at the forefront of the surge in economic
growth for the last twenty years. However, China‟s economic success was at the expense
of degraded environment and diminishing natural resources. According to China Green
National Accounting Study Report 2004 (SEPA and NBS 2006),5 the financial loss
caused by environmental pollution was 511.8 billion CNY (equivalent to 66.3 billion
USD), accounting for 3.05% of national GDP in 2004. The growing economy has
particularly made a large impact on the SOC storage. From 1988 to 2000, GDP growth
was estimated to induce farmland loss, deforestation, and idleness respectively by 573,
131, and 498 hectares in China (Li et al. 2010). These changes heavily lowered its SOC
density. In this situation, it is imperative for Chinese policymakers to change
development guidelines from „grow first, clean up later‟ to a more responsible long-term
strategy. Green growth policy can serve for this purpose because it pursues
environmentally sustainable economic development and generates co-benefits for carbon
sequestration and emission abatement.6 However, it is costly to develop green growth
strategies in the short run because part of the resources would be allocated to design and
implement policy instruments, including taxes, regulations, incentives for clean
technologies, and funding for the basic research. These instruments would inevitably
result in an economic slowdown.
In this section, we intend to evaluate the welfare losses associated with land conversion
and soil carbon sequestration in the context of green growth policies. To this end, we
assume the structure of land-use in the past would continue to work in the future and
simulate land-use changes and SOC flows caused by economic growth for each five-year
interval during the period 2001-2050 by coupling the empirical land-use change model
and SOC density model. The baseline simulation uses actual observations in 2000
throughout the simulation period. It provides a benchmark to measure the effects of GDP
growth on land-use changes and SOC flows. We design ten alternative scenarios (Table
3.2), in which the annual GDP growth rates range in 0.5% reduction from 10% to 5.5%
80
for the first decade and then decline by 1% per five-year. We assume that there was a
growth rate floor (2.0%). The remaining variables are held at their historically observed
values in 2000.
Table 3.2. Description of Scenarios with different Annual GDP Growth Rates
Time interval
2001-05 2005-10 2010-15 2015-20 2020-25 2025-30
Baseline
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
Scenario 1
10.0%
10.0%
9.0%
8.0%
7.0%
6.0%
Scenario 2
9.5%
9.5%
8.5%
7.5%
6.5%
5.5%
Scenario 3
9.0%
9.0%
8.0%
7.0%
6.0%
5.0%
Scenario 4
8.5%
8.5%
7.5%
6.5%
5.5%
4.5%
Scenario 5
8.0%
8.0%
7.0%
6.0%
5.0%
4.0%
Scenario 6
7.5%
7.5%
6.5%
5.5%
4.5%
3.5%
Scenario 7
7.0%
7.0%
6.0%
5.0%
4.0%
3.0%
Scenario 8
6.5%
6.5%
5.5%
4.5%
3.5%
2.5%
Scenario 9
6.0%
6.0%
5.0%
4.0%
3.0%
2.0%
Scenario 10
5.5%
5.5%
4.5%
3.5%
2.5%
2.0%
2030-35
0.0%
5.0%
4.5%
4.0%
3.5%
3.0%
2.5%
2.0%
2.0%
2.0%
2.0%
2035-40
0.0%
4.0%
3.5%
3.0%
2.5%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2040-45
0.0%
3.0%
2.5%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2045-50
0.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
2.0%
Simulations are run at a 10-km-grid scale (equivalent to 10,000 hectares). For
each time interval, the probability of choosing use l for every grid i is a joint probability
depending on the unconditional probabilities in the last period and the conditional
(transition) probabilities between the last and current periods. The formula is presented in
equation (3.8).7
(3.8)
Pil  t    k 1 Pik  t  5 Pil|k  t ,
6
t  2005, 2010,
, 2050.
The total area of each use for each time interval is calculated through summing the choice
probabilities over all grids and multiplying the summations by 10,000 hectares (formula
(3.9)).
(3.9)
Areal  t   10,000  i 1 Pil  t ,
n
t  2005, 2010,
, 2050.
For each time interval, the SOC density of every grid i is estimated as the product of the
SOC density in 2000 and the expected SOC density change due to land conversion, as
shown in formula (3.10).
(3.10)
SOCDi  t   SOCDi  2000   exp

6

ˆ  Pil  t   Pil  2000   ,
l 1 il
t  2005, 2010,
, 2050,
81
where ˆil is the coefficient estimate of land use dummies in SOC density model.
Likewise, the total SOC at time period t is computed by summing SOC densities across
all land grids and multiplying by 10,000 hectares (formula (3.11)).
(3.11)
SOC  t   10,000  i 1 SOCDi  t ,
n
t  2005, 2010,
, 2050.
The procedure is applied to every scenario. We subtract the values obtained from
formulas (3.9) and (3.11) under the baseline from the corresponding values under every
GDP-growth scenario. The differences are land-use changes and SOC flows driven by
economic growth.
Figure 3.3. The area of land by use in the baseline scenario.
Figure 3.3 illustrates the area of land by use in the baseline scenario. As is
shown, the areas of unused land, farmland, grassland, and forestland are relatively large
in China, ranging from 138 to 195 million hectares in 2000. In contrast, the urban and
water areas are small, covering 3.8 and 15.7 million hectares, respectively. Under the
82
baseline scenario, the largest change occurs in urban land, with an increase from 3.8 to
14.7 during the 50-year simulation period. Meanwhile, the areas of land in cultivation,
forest, and pasturing decline. Specifically, farmland increases in the first five years but
begins to decline afterwards. Grassland experiences the greatest absolute loss by 7
million hectares. Generally speaking, except for urban area, changes in most uses are
relatively small under the baseline simulation.
Figure 3.4. The area of land by use in the scenario of initial 10% GDP growth rate.
Once economic growth is considered, e.g., the annual GDP grows at 10% in the
first decade followed by 1% reduction in the annual rate every five-year, unused land
almost doubles from 138 to 239 million hectares throughout the simulation period (figure
3.4). Most of the increase is from declines in farmland, forestland, and grassland. Despite
an increase in the first five years, farmland suffers the largest absolute loss by 55.5
million hectares, which is 13 times as many as the loss in the baseline. Following
farmland, forestland and grassland experience the second and the third greatest loss by
83
31.4 and 27.3 million hectares, respectively. The losses accelerate temporally though
GDP grows at a diminishing rate. The general pattern of land-use changes is similar at
other levels of the annual GDP growth rate. Conversions of farmland, forestland, and
grassland to idleness lead to SOC losses.8 Figure 3.5 pictures negative flows of SOC
relative to the baseline for ten levels of annual GDP growth rates. It shows that more
carbon runs off as the growth rate increases. The variation of carbon flows is large in
different growth rates. For example, by the end of the simulation, the SOC runoff in the
scenario of the fastest GDP growing (20.7 million Mg C) is nearly twice more than the
outflow in the scenario of the slowest GDP growing (7.5 million Mg C).
Figure 3.5. The flow of soil organic carbon relative to the baseline for different GDP growth rates.
In the context of discrete land-use choice model, the usual techniques for
measuring welfare effects must be modified since the welfare properties are more
complicated. In this essay we apply the “logsum” formula derived by Small and Rosen
(1981) to compute the expected compensating variation (CV) associated with land
conversion for changes in GDP growth rate.9 As an approximate measure of consumer
84
surplus in the (multinomial) logit model framework, the “logsum” has been used in the
transport literature (transport demand) and urban literature (residential location, see
Phaneuf et al. 2008). But as far as we know, it has not been applied in land use studies.
Equation (3.12) presents the expression of the “logsum” formula. For a land grid
i with initial use k at time t , the compensating variation (CV) for a change in GDP
growth rate is
(3.12)
  6 exp Vis|k  t   

CVik  t   ln  6s 1
  exp Vis|k  t   
 s 1

V  I 
I
t  2005, 2010,
,
, 2050 ,
where Vis|k  t  and Vis|k  t  are the deterministic components of utility of converting
patch i from use k to s before and after the change in growth rate respectively, and I
represents household income. Hence V  I  I expresses marginal utility of income (or
money), which is assumed constant across agents and land-use choices and over time.
The estimates of V  I  I is 1.49  105 . Considering the randomness of initial use in
every time interval, the expected CV for patch i at time t is computed as
(3.13)
CVi  t    k 1 CVik  t   Pik  t ,
6
t  2005, 2010,
, 2050 .
Summing CV across all individual land grids, we obtain the total expected CV for a
change in annual GDP growth rate at time t (formula (3.14)).
(3.14)
CV  t   i 1 CVi  t ,
n
t  2005, 2010,
, 2050 .
Figure 3.6 portrays the expected CV relative to the baseline for ten levels of annual GDP
growth rates along time. We found that agents‟ welfare associated with land conversion is
improved as the growth rate increases. Specially, relative to the baseline, the net welfare
gain in the scenario of the fastest GDP growing (745 million CNY) is six times as many
as the net gain in the scenario of the slowest GDP growing (122 million CNY) by the end
of the simulation.
We compute the present values of total welfare gain and total carbon loss for
each scenario throughout the simulation period with three levels of social discount rate
85
Figure 3.6. Time series plot of expected logsum welfare gains for different GDP growth rates relative
to the baseline.
(2%, 4%, and 6%). We use annualized equivalents to express them (Richards and Stokes
2004; Stavins 1999). The annualization indicates that marginal damages caused by
additional units of atmospheric carbon are constant and that benefits and costs are to be
discounted at the same rate. Consider the Chinese government implemented a mix of
policy instruments to achieve green development. These instruments might include
pollution taxes, natural resource charges, emission standards, regulations in the
production and use of toxic chemicals, motivating end-of-pipe technology innovations,
funding for fundamental R&D, and so on. Implementation of such policies would slow
down economy growth to different extents and we assume the economy decelerate from
scenario 1 to scenario 10. Figures 3.7-3.8 illustrate the expected marginal and average
welfare losses associated with soil carbon sequestration by discount rate, which are the
main results of this essay.
Moving from scenario 1 to scenario 10 could generate up to 0.7-1.1 million
annualized Mg C and 22.2-37.4 million CNY losses of welfare for each year throughout
86
Figure 3.7. The marginal welfare losses of soil carbon sequestration by discount rate under the Green
Growth policy scenario.
the simulation period. Both marginal and average curves are downward-sloping, implying
a concave total welfare curve. The marginal curves are convex at low levels of welfare
losses (below 25 CNY/Mg) and approximately concave throughout most of its range.
Empirically, we found that higher discount rates reduce the annualized carbon flows, the
annualized marginal and average losses of welfare. Hence both curves shift inwards (or
leftwards) as the discount rate goes up. For example, in sequestering 0.7 million Mg SOC
per year, the annualized marginal (or average) welfare loss per Mg C declines from ¥34.3
(¥42.7) to ¥21.8 (¥37.8) and ¥13.1 (¥37.1) at discount rates of 2%, 4%, and 6%,
respectively.
We compare these results with the marginal costs of agricultural practice,
including conservation tillage and reduction in fellow, estimated in previous studies of
soil carbon sequestration. To generate about 1 million Mg C per year, the estimated
marginal welfare loss is ¥15.3/Mg (equivalent to $2.25/Mg) in our study, which is lower
than the costs estimated by previous literature in which the marginal costs are $11.6/Mg
87
(Feng et al. 2006), $50/Mg (Antle et al. 2007), $20-100/Mg (Capalbo et al. 2004), and
$190-270/Mg (Pautsch et al. 2001). With these results, we see a promising strategy
oriented by green growth to sequester SOC. In addition, the total social welfare loss
would be much smaller if the co-benefits of green growth policy were considered.
Figure 3.8. The average welfare losses of soil carbon sequestration by discount rate under the Green
Growth policy scenario.
88
CONCLUSIONS
In this essay, we explored two questions: 1) What is the association between
land conversion and SOC density in China? 2) What is the welfare loss associated with
soil-based carbon sequestration through implementing the green growth policy? For this
purpose, we compiled a unique, national-scale dataset which includes high quality land
use data and soil profile data. We developed two models for analysis. One is a statistical
model to explore the association of SOC density with six major land uses; the other is a
multinomial logit model to analyze the effects of socioeconomic factors on land
conversion among six uses. We answered these questions by estimating the models and
generating a series of simulations with the estimation results.
In the SOC model, we found that the SOC density is higher in forest and grass
lands and lower in unused land on average. The spatial analysis provides strong evidence
for the existence of spatial nonstationarity in the parameter estimates and spatial
autocorrelation in error term. In the land-use change model, we found that land-use
changes were affected by socioeconomic factors, including county GDP, population,
agricultural investment, and highway density. In a county with higher GDP, land is less
likely to be used for cultivation. In a county possessing larger population or denser
highway, cultivation and afforestation are more likely to take place than pasturing. Public
agricultural investment increases the probabilities of agricultural use (farmland and
grassland).
Combining results of two models, we predicted the effects of GDP growth on
land use, SOC storage, and social welfare. We found that GDP growth leads to farmland
and grassland loss, deforestation, and idleness, which accelerates soil carbon runoff. We
estimated the expected marginal and average welfare losses associated with soil carbon
sequestration by generating green growth policy simulations from 2001 to 2050. We
found that the policy could sequester up to 0.7-1.1 million annualized Mg SOC and 22.237.4 million CNY losses of welfare per year. The marginal and average welfare loss
curves are downward-sloping. The marginal welfare loss is approximately ¥15.3/Mg
(equivalent to $2.25/Mg) for sequestering about 1 million Mg SOC per year. A
89
comparison of the results reveals that soil-based carbon sequestration, oriented by the
green growth policy, merits consideration. Caution should be exercised when interpreting
the welfare effect. It measures the expected compensating variation for policy-induced
land-use changes, which is not equivalent to the total social benefits and/or losses
resulting from the green growth policy.
The contribution of this essay to literature is twofold. First, it is the first
economic application in soil carbon sequestration using detailed, national-scale Chinese
data. By contrast, previous research is typically conducted by scientists. Second, it
applies the “logsum” formula to evaluate the welfare effects of policy-oriented land
conversion. The formula allows measuring consumer surplus in the context of
multinomial logit models. To the best of our knowledge, no one has used this method for
welfare analysis in the land use literature.
90
ENDNOTES
1
E.g., Denitrification-Decomposition (DNDC) model and Integrated Terrestrial
Ecosystem C-budget (InTEC) model.
2
A covariate is a secondary variable that can affect the relationship between the
dependent variable and other independent variables of primary interest.
3
We also have soil clay content. The summation of soil loam, sand, and clay content
equals one.
4
In some circles, the number of parameters is greater than the number of observations
which leads to missing estimates. We exclude those incomplete estimates in the following
analysis.
5
As an experiment in national accounting, the Green GDP effort collapsed in failure in
2007, when it became clear that the adjustment for environmental damage had reduced
the growth rate to politically unacceptable levels, nearly zero in some provinces.
6
Green growth can be seen as a way to pursue economic growth and development, while
preventing environmental degradation, biodiversity loss, and unsustainable natural
resource use. It builds on existing sustainable development initiatives in many countries
and aims at identifying cleaner sources of growth, including seizing the opportunities to
develop new green industries, job and technologies, while also managing the structural
changes associated with the transition to a greener economy (OECD 2010).
7
For any land grid starting in urban uses, the probabilities of converting to other uses
equal zero provided the assumption of irreversible urbanization.
8
The estimation results of SOC density model show that ceteris paribus, the SOC density
is highest in forestland and grassland but lowest in unused land on average.
9
Small and Rosen (1981) extended the conventional methods of applied welfare
economics to discrete choice models. They showed that the expected CV can be
computed as an integral of the Hicksian choice probabilities.
91
ACKNOWLEDGMENTS
We thank the National Science Foundation of China (70873118) and the
Chinese Academy of Sciences (KZCX2-YW-305-2) for the financial support to generate
the dataset used in this study.
92
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96
CHAPTER 4
DECOMPOSING THE CHANGE OF CO2 EMISSIONS IN CHINA:
A DISTANCE FUNCTION APPROACH
MAN LI
97
ABSTRACT
This essay examines the sources of change in carbon dioxide (CO2) emissions. It
evaluates the relative contributions of the sources to emission abatement using a new
empirical approach. The method uses the data envelopment analysis (DEA) technique to
decompose emission changes into seven components based on the Shephard output
distance function. It allows for cross-sectional analysis under flexible data requirement.
The method accounts for factors that increase carbon emissions, as well as decrease them.
With the application of decomposing change in China‟s CO2 emissions at the provincial
level between the years 1991 and 2006, the study finds that 1) GDP scale effect accounts
for the majority of emission increments; 2) the emission index associated with capital is a
dominant contributor to emission abatement; and 3) the effects of technical change in
production and change in the GDP-composition by sector play positive roles in shrinking
emissions.
98
INTRODUCTION
One of the most important targets in climate change policy is to reduce carbon
dioxide (CO2) emissions. CO2 emissions are going up at a rapid rate. For example, China
surpassed the United States to become the largest CO2 emitter of the world in 2006,
releasing nearly 1.7 million petagrams of carbon (Pg C) into the atmosphere, which is 2.4
times more than the amount it emitted in 1991 (Marland et al. 2009). Under the 1997
Kyoto Protocol, Annex I countries (i.e., most industrialized countries) committed
themselves to cutting their aggregate anthropogenic CO2 equivalent emissions by at least
5% below 1990 levels for the time periods 2008 to 2012. Kyoto‟s Clean Development
Mechanism also allows Annex I countries to implement emission-reduction project in
developing countries to acquire carbon credits and offset their off-quota of emissions.
Consequently, how to reduce CO2 emissions presents great challenges for policy makers
of many countries.
It is of theoretical and practical significance to understand what factors have
driven the change of CO2 emissions. Many of the previous studies used an index
decomposition analysis (IDA).1 Examples include Ang and Pandiyan (1997), Han and
Chatterjee (1997), Sun (1999), Viguier (1999), Wang et al. (2005), and Wu et al. (2005).
A majority of findings point out that gross domestic product (GDP) growth is a main
source of CO2 emission increments in most countries, while declining energy intensity
contributes to declines in emissions over the time periods 1960 to 2000. The findings of
studies on China are consistent with those in the global context (Ang and Pandiyan 1997;
Wang et al. 2005; Wu et al. 2005).
The major purpose of this essay is to propose an alternative approach to examine
the sources of CO2 emissions change and evaluate the relative contributions of the
sources to emission abatements. To achieve the objective, we decompose the change into
seven components based on the Shephard output distance function under a joint
production framework. We apply this method to disaggregate the province-level changes
of CO2 emissions in China from 1991 to 2006. Major findings are 1) GDP scale effect
accounts for the majority of emission increments; 2) the emission index associated with
99
capital is a dominant contributor to emission abatement; and 3) the effects of technical
change in production and GDP-composition change by sector play positive roles in
shrinking emissions.
Distance function approach is recently applied in the literature for decomposing
the change in undesirable production outputs (Pasurka 2006; Zhou and Ang 2008). Those
applications differ in the assumption of measuring technical efficiency. For example,
Pasurka (2006) assumes an output-based radial measure which scales on good and bad
outputs symmetrically; Zhou and Ang (2008) use input distance functions and output
distance functions that model include undesirable output to measure changes in efficiency.
In this essay we assess productive efficiency through desirable output subvector by
holding bad output and inputs constant. This assumption is reasonable because
improvement of productive efficiency associated with good outputs is much more
common in practice than that associated with bad outputs. Besides, in contrast to Pasurka
(2006) and Zhou and Ang (2008) where the effects of all production inputs are treated
jointly, our approach possesses a unique feature which allows isolating the impact of
every input on emission change. Application of the isolating technique is found in energy
studies (Ang et al. 2004; Wang 2007). But to the best of our knowledge, it has not been
applied in emission analysis.2
The essay also contributes to the literature in two aspects. First, it uses crosssectional data observed at the provincial level, which overcome the limitation in the
existing literature where decomposition typically relies on detailed sector-level data.
Second, it investigates the sources of CO2 emission changes in China in the recent sixteen
years (1991-2006). Most of previous analyses are conducted in sample periods of pre2000.
The remainder of this essay is organized in the following manner. Section 2
describes the methodology. Section 3 applies the approach for decomposing China‟s CO2
emission changes between 1991 and 2006. The final section concludes.
100
METHODOLOGY
In the context of joint production settings, the technology at any time period t is
described by the set:
(4.1)
S  t    K , L, E, Y, C; t  :  K , L, E  can produce  Y, C  at time t ,
where K , L, E  denote production inputs of capital, labor, and energy, respectively;
Y 3 represent all desirable outputs of GDP; C  is undesirable outputs of CO2
emissions joint with Y . We group GDP into three major sectors – agriculture, industry,
and service, and use elements Y1 , Y2 , and Y3 to represent outputs from those sectors (i.e.,
Y  Y1 , Y2 , Y3  ). CO2 emissions are generated by agriculture, industry, and service that
cover all production activities in an economy. The set S  t  satisfies standard properties
of production set such as null-jointness, weak disposability of joint outputs (good and
bad), strong disposability of inputs and good outputs, which suffice to define a Shephard
output function. In words, the idea of null-jointness means that if some good outputs are
produced then some bad outputs must also be produced; weak disposability says that it is
costly to reduce the bad outputs because abatement uses resources that otherwise could
have been used to maintain or increase desirable outputs; strong disposability implies that
1) good outputs can be disposed of without cost and 2) an increase in inputs cannot
congest outputs. Appendix C gives descriptions of the properties through the production
set.3
Eq. (4.2a) provides the definition of the Shephard output distance function for
the subvector of good outputs Y , which measures how far the observed production of
good outputs is from the maximum potential production at time t given inputs, the bad
output, and technology.
(4.2a)


Yt t 
 

Dy  K t , Lt , E t , Yt , C t ; t   inf  :  K t , Lt , E t ,
, C   S t  ,




 

Alternatively, we can define a subvector distance function for mixed period analysis,
101
 
Y 

Dy  K  , L , E , Y , C ; t   inf  :  K  , L , E ,
,C

 

(4.2b)



  S t  ,



where the superscript  represents the time period of observed production activity and t
represents the time period of technology, e.g.,   t  1 . By definition, it is easy to verify
that
the
subvector
distance
function
satisfies
three
properties:
first,
    Y  
, C   S  t  ; second, nonincreasing in
for
Dy  K , L , E , Y , C ; t   1
K ,L ,E ,








the input set  K  , L , E , C  ; third, nondecreasing, homogeneous of degree 1 and
convex in good outputs Y .
Taking production technology at time period t (i.e., S  t  ) as a reference, the
change of CO2 emissions between time periods t and t  1 is decomposed as in the
following equation,
(4.3)
Dy  K t , Lt , E t , Y t , C t ; t 
C t 1 Y1t 1  Y2t 1  Y3t 1


Ct
Y1t  Y2t  Y3t
Dy  K t 1 , Lt 1 , E t 1 , Y t 1 , C t 1 ; t 

C t 1
Y1t 1 Y2t 1 Y3t 1
 Dy  K t 1 , Lt 1 , E t 1 , Y t 1 , C t 1 ; t 
Ct
Y1t Y2t Y3t
 Dy  K t , Lt , E t , Y t , C t ; t 
Dy  K t , Lt , E t , Y t , C t ; t 
Y1t 1  Y2t 1  Y3t 1


Y1t  Y2t  Y3t
Dy  K t 1 , Lt 1 , E t 1 , Y t 1 , C t 1 ; t  1
Dy  K t 1 , Lt 1 , E t 1 , Y t 1 , C t 1 ; t  1 C t 1  Dy  K t 1 , Lt 1 , E t 1 , y t 1 , C t 1 ; t 


Dy  K t 1 , Lt 1 , E t 1 , Y t 1 , C t 1 ; t 
C t  Dy  K t , Lt , E t , y t , C t ; t 
: SC  TE  TC  t  1 
C t 1  Dy  K t 1 , Lt 1 , E t 1 , y t 1 , C t 1 ; t 
C t  Dy  K t , Lt , E t , y t , C t ; t 
,


Y3
Y1
Y2
,
,
where y  
 , representing proportions of output from
 Y1  Y2  Y3 Y1  Y2  Y3 Y1  Y2  Y3 
the three sectors in GDP; superscript t and t  1 denote time periods. The decomposition
102
(4.3) indicates that the emission change between periods t and t  1 is the product of four
multipliers. The first one, named as scale effect ( SC ), is the ratio of GDP between the
two time periods. The second one is technical efficiency change effect ( TE ), which
captures the change in the distance between the observed production and its
corresponding potential production between time t and t  1 . The third one is defined as
technical change effect ( TC  t  1 ), measuring the shift of frontier technology by taking
production at time period t  1 as a reference. The technical efficiency change effect and
technical change effect are the components of the Malmquist productivity change index
and are widely studied in the area of productivity and efficiency analysis (Färe et al.
1994). Although technology scales solely on the good output Y , TE and TC can
provide a measure of changes in CO2 emissions associated with technical change and
changes in technical efficiency, because emission changes will affect the shape of
production frontier and the location of individual production in the production set.
When the assumption of constant returns to scale (CRS) is imposed on the
reference technology S  t  , the last multiplier on the right-hand side of Eq. (4.3) can be
reorganized such that
C t 1  Dy  K t 1 , Lt 1 , E t 1 , y t 1 , C t 1 ; t 
(4.4)
C t  Dy  K t , Lt , E t , y t , C t ; t 


K t 1
C t 1
Dy

where k 
Dy

t 1
, CLt 1 , CEt 1 , y t 1 , CC t 1 ; t
t 1
Kt
Ct
t 1
t
t
t
, CLt , CE t , y t , CC t ; t

Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t 
Dy  k t , l t , et , y t ,1; t 

,
K
L
E
, l  , and e  , denoting capital-carbon ratio, labor-carbon ratio, and
C
C
C
energy-carbon ratio, respectively. This rearrangement reveals a desirable property of the
Shephard output distance function under CRS assumption, i.e., the function is
homogeneous of degree -1 in
K , L , E ,C  .
t
t
t
t
As we previously discussed, the
assumption of weak disposability of joint outputs GDP and CO2 emissions means that
cutting emissions uses resources which otherwise could have been used to maintain or
increase good outputs. Hence changes in k, l, and e respectively reflect changes in the
103
allocation of each individual input between good outputs production and CO2 emission
abatement. For example, ceteris paribus, any increase in k, l, and e will result in reduction
of CO2 emissions. Due to the direct link between energy consumption and CO2 emission,
changes of e also capture the effect of fuel switching on CO2 emissions.4
Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t 
Dy  k t , l t , et , y t ,1; t 
is an Malmquist-type productivity index with the
reference technology of S  t  . It measures a joint effect that results from changes in the
ratios of each of three inputs to carbon and GDP-composition change by sector between
time periods t and t  1 . We further separate
Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t 
Dy  k t , l t , et , y t ,1; t 
into four parts by
isolating the effects of changes in k , l , e , and y between the two time periods. We
write the first component in Eq. (4.5) below to illustrate the structure and present the
whole decomposition of index
Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t 
Dy  k t , l t , et , y t ,1; t 
in Appendix D.
1 24
(4.5)
 D k t 1 , l t 1 , et 1 , y t 1 ,1; t 6  D k t 1 , l t , et 1 , y t 1 ,1; t  2 
  y 
 
 y 
t
t

1
t

1
t

1
t
t
t

1
t

1
 Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t   
 
 

2
2
 
t 1 t 1
t
t 1
t 1 t 1
t 1
t



D
k
,
l
,
e
,
y
,1;
t
D
k
,
l
,
e
,
y
,1;
t




y
y
 
 
 
  D k t , l t 1 , et , y t 1 ,1; t    D k t , l t 1 , et 1 , y t ,1; t  
   y

  y
KC  t   

2
2
t 1 t
t
t 1
t 1 t
t 1
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  

 

 

t t
t
t 1
t t
t 1
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  



2
6
  Dy  k t 1 , l t 1 , et , y t ,1; t    Dy  k t 1 , l t , et , y t ,1; t  

 

 

t t 1
t
t
t t
t
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  

.
We name KC  t  as emission index associated with capital. It measures the effect of
change in capital-carbon ratio on CO2 emissions. The remaining three components,
denoted as LC  t  , EC  t  and CM  t  , can be derived in a similar way. Thus we have
104
(4.6)
Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t 
Dy  k t , l t , et , y t ,1; t 
: KC  t   LC  t   EC  t   CM  t  .
The definitions of LC  t  and EC  t  are analogs to that of KC  t  , respectively
measuring the emission indices associated with labor and energy. CM  t  is defined to
measure GDP composition effect.
Combining equations (4.3), (4.4) and (4.6) gives that
(4.7)
C t 1
: SC  TE  TC  t  1  KC  t   LC  t   EC t   CM t  .
Ct
Notice that decomposition (4.7) uses technology at time t as a reference.
Alternatively, change in CO2 emissions can be decomposed with the reference of
production technology at time t  1 (i.e., S  t  1 ).
(4.8)
C t 1
: SC  TE  TC  t   KC  t  1  LC  t  1  EC  t  1  CM  t  1 .
Ct
To avoid choosing an arbitrary benchmark, we calculate the geometric mean of the right
hand side of equations (4.7) and (4.8) and obtain the fundamental decomposition result of
this essay as shown in Eq. (4.9).
12
12
C t 1
12
12
  SC  SC   TE  TE   TC  t  1  TC  t     KC  t   KC  t  1 
t
C
(4.9)
  LC  t   LC  t  1 
12
  EC  t   EC  t  1 
12
 CM  t   CM  t  1
12
: SC  TE  TC  KC  LC  EC  CM .
The decomposition result in (4.9) demonstrates that there are seven indices
accounting for the change of CO2 emissions between time periods t and t  1 : GDP scale
effect, technical efficiency change effect, technical change effect, emission indices
associated with capital, labor, and energy, and GDP composition effect. For any one of
them, it will contribute to shrinking CO2 emissions if its value is less than one.
To calculate the value of the seven indices, this study constructs a production set
with an activity analysis model as described in Appendix E. We use data envelopment
analysis (DEA) to compute the value of every output distance function in (4.8). DEA is a
105
nonparametric linear programming approach, which enables us to relax functional
assumptions on production technology. Based on definition (4.2b) the relevant linear
programming problem is specified as in the optimal problem (4.10).
D K
y

, L , E , Y , C  ; t 
s.t.
(4.10)

t
n 1 n

N
t
n 1 n

N

1
 max 
z Ynt   Y
z Cnt  C 
z K nt  K 
N
t
n 1 n


z Ltn  L
N
t
n 1 n
z Ent  E
N
t
n 1 n
znt  0, n  1,
, N.
Compared with the index decomposition method, the approach proposed in this
study satisfies all three criteria tests – time-reversal, factor reversal, and zero-value robust
– as suggested in a survey by Ang and Zhang (2000). By contrast, out of ten index
decomposition methods investigated by the survey, only three passed all tests.5
Meanwhile, the present decomposition provides an alternative approach to quantifying
scale effect, composition effect, and technique effect, which are widely used to explain the
environmental Kuznets curve (EKC) since Grossman and Krueger (1993).6 In particular,
SC and CM in the context of this study are respectively equivalent to scale effect and
composition effect applied in EKC literature. The joint effect of TE  TC  KC  LC  EC
in Eq. (4.9) plays the same role as technique effect.
106
APPLICATION
In this section, we decompose the province-level changes of CO2 emissions in
China between 1991 and 2006 using the proposed method.
Data
Data on sixteen years 1991-2006 are collected for the empirical study. Province
is the unit of observation and there are 29 observations for each year.7 Variables include
CO2 emissions, capital stock, labor input, energy consumption, and GDP by region. Data
in value terms are measured at the 1990 real 108 Chinese yuan (CNY).
The national CO2 emissions for the time period 1991-2006 are collected from
Marland et al. (2009). However, the actual provincial CO2 emissions do not exist because
measuring emissions from a large amount of widely dispersed sources would be
prohibitively expensive. National Bureau of Statistics of China (NBSC) reports waste gas
emissions (WGE) and industrial waste gas emissions (IWGE) at the provincial level for
the time period 1991-95. After 1995 only IWGE is reported. Therefore we use provincelevel WGE and IWGE as parameters to proxy for provincial CO2 emissions. The approach
follows a recent study by Auffhammer and Carson (2008) on forecasting China‟s CO2
emissions. Appendix F provides the detail on estimation procedure. Column 1 in Table 1
reports the change in CO2 emissions in 29 provinces. It indicates that the emissions
increase in all provinces except for Beijng and Heilongjiang.
There are no official data available for measuring China‟s capital stock at the
provincial level. This essay estimates it with a perpetual inventory approach, which is a
typical way of calculating capital stock in the literature. Start with the initial year of 1978
and for any year t  1979,
(4.11)
, 2006 ,8 capital stock of province n in year t is calculated as
Kt ,n  1    Kt 1,n  I t ,n
 1   
t 1978
K1978,n   s 1979 1   
t
t s
,
I s ,n
where K is capital stock,  is depreciation rate, I is the real value of investment in
fixed assets, and subscript s represents the time period index. According to the formula
107
(4.10), we need to determine the depreciation rate and the initial value of provincial
capital stock in 1978. As in Chow (2002), we adopt a depreciation rate of 4% and assume
it to be constant across provinces. Following the studies of Hall and Jones (1999) and
Henderson et al. (2007), the initial capital stock for province n is estimated as:
(4.12)
K1978,n 
I1978,n
  gn
,
where g n is the geometric mean of growth rates of real fixed investment over the time
period 1978-88 for province n . Time series of investment in fixed assets for the periods
1978-2006 are collected from NBSC (2005; 2006-07). Investment deflators are not
available at the provincial level. We use the same province-specific price index as for
GDP as in Henderson et al. (2007).
Data on labor input and GDP are collected directly from NBSC (2005; 2006-07).
Labor input is measured in the number of employees. GDP consists of three sectors:
primary, secondary, and tertiary industries. Primary industry is composed of farming,
forestry, animal husbandry, and fishery. Secondary industry mainly includes activities of
building, mining, manufacture, electricity and gas production. And those not belonging to
the former two are classified into tertiary industry, e.g., transportation, trade, finance,
education, public service, etc. For convenience, we use Agriculture, Industry, and Service,
respectively, representing three sectors. We collect data on total energy consumption by
region from NBSC (2005) and NBSC and NDRC (1997-99; 2005-07) and use 104
megagrams standard coal equivalent as the unit.
Results and Discussions
Before presenting the final results, it is instructive to give an outline of national
emission trend in China from 1991 to 2006, which is plotted in Fig.4.1. It shows that CO2
emissions peaked for the first time in 1996 then dropped off and touched the bottom in
1998. After China‟s entry to the World Trade Organization (WTO) in 2001, the
emissions went up at a soaring rate of 12% per year.
108
Figure 4.1. National Carbon Dioxide Emissions in China, 1991-2006
Columns 2-7 of Table 4.1 report contributions to CO2 emission change from
GDP scale effect, technical efficiency change effect, technical change effect, and the
effects of changes in capital-carbon ratio, labor-carbon ratio, energy-carbon ratio, and
GDP-composition by sector between 1991 and 2006.9 As shown in column 2, the value of
GDP scale effect ( SC ) is greater than one in all provinces in this study. The annual
contribution of SC to CO2 emission increments is 11.6%, much higher than the values of
other six indices, indicating that SC is a main driver for emission increases. This result is
in line with the findings of previous studies (Wang et al. 2005; Wu et al. 2005; Zhou and
Ang 2008).
Columns 3-4 in Table 4.1 describe the effects of technical efficiency change
( TE ) and technical change ( TC ) on emission change. On average, technical
improvement reduces CO2 emissions by 1.63% per year whereas the impact of technical
109
Table 4.1. Change in CO2 Emissions and its Seven Decomposing Indices, 1991-2006
CO2
SC
TE
TC
KC
LC
EC
Region
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Beijing
0.9327 5.1038 1.0000 0.3509 0.4833 0.8733 0.8603
Tianjin
3.2448 6.1891 1.0000 0.4012 0.7816 1.7809 1.0032
Hebei
3.9790 5.6581 0.9502 0.7950 0.6567 1.5453 1.0004
Shanxi
3.0833 4.8386 0.7303 0.8495 0.7312 1.4066 1.0000
Inner Mongolia
3.1309 6.1853 1.0228 0.7816 0.5769 1.6155 0.9960
Liaoning
1.7890 4.4473 0.9851 0.6069 0.5389 1.3289 1.0000
Jilin
1.1705 4.9314 0.9297 0.7238 0.3950 1.0995 0.9978
Heilongjiang
0.8985 3.8130 0.8926 0.6817 0.4341 0.9550 0.9897
Shanghai
1.8531 5.7876 1.0000 0.3985 0.5782 1.3230 0.9209
Jiangsu
3.0475 7.3023 1.0000 0.6823 0.5599 1.3244 0.9776
Zhejiang
2.7911 7.4071 1.0590 0.7132 0.5518 1.2122 0.8224
Anhui
2.3603 6.0340 0.9803 0.9556 0.4642 1.0890 0.9950
Fujian
3.7017 7.2234 0.9899 0.7523 0.6392 1.3491 0.9586
Jiangxi
2.3234 5.4711 1.0000 1.1411 0.4388 1.1493 0.9819
Shandong
3.0803 6.9385 0.9699 0.7720 0.5723 1.2996 0.9973
Henan
3.2020 5.4488 0.8435 1.2084 0.5801 1.1834 1.0002
Hubei
2.1211 5.1413 1.0870 0.8227 0.4973 1.2243 0.9962
Hunan
1.8173 4.5680 0.9543 1.1318 0.4520 1.0835 0.9925
Guangdong
2.7694 7.1287 1.0000 0.5802 0.6260 1.2571 0.8887
Guangxi
5.5688 5.4680 1.0000 1.2028 0.7741 1.4490 1.0329
Hainan
7.3462 4.6507 1.0000 0.8228 0.9825 2.1505 0.9934
Sichuan
1.8869 4.7069 1.2188 1.0647 0.3765 1.1018 0.9940
Guizhou
1.3191 3.8372 1.1972 1.4104 0.2919 1.0007 0.9920
Yunnan
3.5650 3.9216 1.2739 1.1349 0.6517 1.2359 1.0006
Shaanxi
2.3000 4.4196 0.9910 0.8701 0.5563 1.1415 0.9988
Gansu
1.6829 4.2883 1.2001 0.9900 0.3524 1.0529 0.9983
Qinghai
3.7703 4.1445 1.2969 0.5736 0.9468 1.5437 1.0000
Ningxia
2.9084 4.3732 1.2577 0.7500 0.6435 1.3844 0.9989
Xinjiang
2.8612 3.9363 1.2670 0.6917 0.7445 1.5658 1.0000
Geomean
2.4943 5.1784 1.0293 0.7810 0.5604 1.2769 0.9777
Annual Geomeans
1.0628 1.1159 1.0019 0.9837 0.9621 1.0164 0.9985
# Regions < 1.00
2
0
11
22
29
2
21
# Regions = 1.00
0
0
8
0
0
0
3
# Regions > 1.00
27
29
10
7
0
27
5
CM
(8)
1.4343
0.9357
0.9170
0.9986
0.6821
0.9394
0.8140
0.9439
1.1407
0.8438
0.9071
0.8301
0.8325
0.7516
0.7993
0.8397
0.7605
0.7577
0.9574
0.7308
0.9147
0.7492
0.7025
0.7802
0.9516
0.8919
0.8367
0.7923
0.7114
0.8564
0.9897
27
0
2
efficiency change is negligible. The values of TE and TC are distributed unevenly
across regions. For the richest metropolises, Beijing and Shanghai, the contribution of
110
TC to abatement is far above the national average. This is most likely a result of highly
concentrated top national universities and research institutes in these cities. By contrast,
the contribution of TE is trivial because their economies stay on the production frontier
for the whole sample periods. Guangdong presents a similar case as a wealthy coastal
province where there is the largest amount of special economic zones in the 1990s. With
the advantage of location close to Hong Kong and being received preferential treatment
from the central government, Guangdong experienced technical progress. However,
being on the frontier at the start of the sample periods prevents it from improving in
efficiency. However, interior regions tell a different story. In some midland provinces
such as Shanxi and Henan, TE plays a positive role in curtailing CO2 emissions as
expected. Located adjacent to coastal provinces, those regions benefit from technological
catch-up. But it is not true for western provinces including Yunnan, Gansu, Qinghai,
Ningxia, and Xinjiang, which are outlying areas in the South/North-west. Geographical
disadvantage makes them fall behind the forefront of economic reform.
Emission indices associated with capital ( KC ), labor ( LC ), and energy ( EC )
are reported in columns 5-7 of Table 4.1. It is easy to find that KC plays a dominant role
in emission abatement over the 16 years. In contrast to KC , LC raises the emissions by
1.64% per year and becomes the second largest source of increments in emissions. The
value of EC is almost equal to one, implying that this index has a trifling effect on
emission changes. It is in line with the findings of previous study Ang (1999) where
carbon factor (i.e., the ratio of CO2 emissions to energy consumption) is proposed to be a
less useful indicator in the study of climate change.
The last column of Table 4.1 reports GDP composition effect ( CM ) on
emissions change. As is shown, CM results in a reduction in emissions of 1.03%
annually. In particular, it shrinks more CO2 emissions in the interior provinces, such as
Inner Mongolia, Guangxi, Guizhou, and Xinjiang. One reason is that structural
transformation from industry to service tends to curtail industrial emissions. It should be
noticed that Beijing and Shanghai are the only two regions with a value of CM greater
than one. As is known, energy intensity in agricultural sector is much lower than that in
111
industrial and service sectors. The agricultural proportions of GDP are extremely low in
these two metropolises, which leads to higher values of CM .
Table 4.2. Geometric Means of Annual Changes for Each Consecutive Two-year Period, 1991-2006
Two-year pairs
1991-1992
1992-1993
1993-1994
1994-1995
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
2004-2005
2005-2006
Annual Geomeans
Non-chaining
Annual Geomeans
CO2
SC
TE
TC
KC
LC
EC
CM
(1)
1.0396
1.0568
1.0385
1.1050
1.0719
1.0254
0.7835
1.0441
1.0852
1.1407
1.0917
1.1207
1.1665
1.1245
1.1076
1.0628
(2)
1.1392
1.1512
1.1334
1.1172
1.1157
1.1040
1.0925
1.0844
1.0923
1.0957
1.1059
1.1184
1.1314
1.1267
1.1326
1.1159
(3)
1.0238
1.0179
0.9931
1.0001
0.9900
0.9988
1.0005
1.0112
1.0006
1.0066
1.0060
0.9927
0.9918
0.9929
1.0037
1.0019
(4)
0.9668
0.9655
0.9654
0.9843
0.9982
0.9781
0.9677
1.0239
1.0175
1.0060
0.9985
1.0113
0.9845
1.0200
0.9910
0.9917
(5)
0.9517
0.9389
0.9345
0.9865
0.9509
0.9500
0.8061
0.9495
0.9673
0.9990
0.9800
0.9874
1.0027
0.9782
0.9712
0.9687
(6)
1.0026
1.0150
1.0047
1.0180
1.0118
1.0124
0.9434
0.9935
1.0151
1.0333
1.0128
1.0185
1.0443
1.0297
1.0212
1.0010
(7)
0.9884
0.9979
0.9973
0.9961
1.0105
0.9949
0.9858
1.0023
1.0060
1.0115
0.9944
0.9900
1.0011
0.9957
1.0042
0.9955
(8)
0.9775
0.9821
1.0208
1.0044
1.0001
0.9936
0.9881
0.9835
0.9879
0.9846
0.9957
1.0025
1.0073
0.9826
0.9871
0.9931
1.0628
1.1159
1.0019
0.9837
0.9621
1.0164
0.9985
0.9897
The proposed method also applies to consequential years. Because choice
between chaining (based on time series data) and non-chaining (based on the data for the
beginning and ending years) analysis will affect the results of five factor TC , KC , LC ,
EC , and CM ( SC and TE will not be changed if we accumulate the chain-based results
by taking their annual geometric means), we alternatively decompose the change of CO2
emissions for each consecutive two-year period between 1991 and 2006.10 Table 4.2
reports the geometric means of 29 observations for every period and the annual geometric
means for the whole period from 1991 to 2006. In comparison with the annual geometric
means obtained based on the beginning year 1991 and the ending year 2006, we find that
the decomposition results are robust to the selection between chaining and non-chaining
112
analysis. The alternative chain-based decomposition slightly changes five factors in
magnitude but does not affect the results by much. The non-chaining analysis is less
computational intensive because only 36 subvector distance functions are needed to be
estimated. By contrast, the chaining analysis requires calculating 540 distance functions
( 540  36 15 periods ). Hence, we recommend adopting non-chaining analysis when
applying the proposed approach to long-term decomposition.
The sub-period 1997-98 deserves attention. As opposed to the long-run trend,
the average provincial emissions fall down by 21.7% from 1997 to 1998. Three factors –
KC , LC , and TC – contribute to the sudden drop. In particular, KC (= 0.8061) reduces
as many as 19.4% of CO2 emissions in the one-year interval, which is far lower than its
annual geometric means 0.9687 (chaining) and 0.9621 (non-chaining); LC (= 0.9434)
plays a positive role in shrinking emissions by accounting for 5.66% of the abatements;
TC (= 0.9677) drives the decline by 3.3%, lower than the annual average levels 0.9917
(chaining) and 0.9837 (non-chaining). This phenomenon relates to policy oriented
industrial transition from energy intensive technology to capital/labor intensive
technology and policy oriented changes in resource allocation between increasing good
outputs and reducing bad outputs. In 1997 the Chinese regulatory authority passed the
Energy Conservation Law, which provides broad guidance for the establishment of
energy efficiency policies. Besides, the concurrent industrial restructuring caused the
shutdown of thousands of inefficient enterprises including small-scale mines and power
plants. The reform shrank energy consumption and hence led to a sudden fall in CO2
emissions because of the direct link between energy consumption and emissions.
Meanwhile, capital and labor inputs were increased. The increments supplemented the
loss of energy inputs in production on the one hand and were allocated to emission
reduction on the other hand.
113
CONCLUDING COMMENTS
This essay presents an alternative approach to index decomposition method for
disaggregating change of CO2 emissions in the context of joint production settings. Data
requirements for the proposed method are much flexible (e.g., it allows using crosssectional data observed at provincial level). Hence, it overcomes the limitation of index
decomposition approach which typically relies on detailed sector-level data. Without
specifying any functional form for heterogeneous production functions across regions, it
can isolate and quantify the effects of changes in GDP scale, production technology,
technical efficiency, capital-carbon ratio, labor-carbon ratio, energy-carbon ratio, and
GDP-composition by sector.
We apply the proposed method to decomposing province-level changes in CO2
emissions of China into seven components for the time period 1991-2006. Caution must
be exercised when interpreting the results, which are influenced by the quality of some of
the data. Nevertheless, the results provide strong evidence that the GDP scale effect is the
main driver behind rising emissions, whereas the emission index associated with capital,
technical change effect, and GDP composition effect are joint contributors to emission
abatement. Specifically, capital accumulation (i.e., emission index associated with capital)
plays a dominant role across the country. Regional analysis indicates an uneven pattern
within China: coastal provinces favor technical improvement whereas interior (midland
and western) regions enjoy structural transition of GDP; midland areas benefit from
technical efficiency catching-up while western provinces fall behind the technology
frontier.
Our decomposition results offer informative implications for policymakers to
reduce CO2 emissions in China. The interior regions of China have great potential of
technical efficiency improvement by introducing advanced technology and learning
management experiences from coastal provinces. As the key driver of China‟s overall
economic growth (Henderson et al. 2007), capital accumulation will still be the most
important contributor to abatement across the country in the foreseeable future. But in the
long run, China should rely more on technical progress to reduce CO2 emissions.
114
ENDNOTES
1
Ang and Zhang (2000) provide an excellent survey on the methods and application areas
of Index decomposition analysis.
2
See Siegel (1945) for a general discussion.
3
Färe et al. (1989) and Färe et al. (2001; 2007) give the details on definitions and
discussions for those properties.
4
Our approach can be extended by using more detailed energy data, e.g., electricity and
non-electricity or coal, gas, and petroleum products, to capture more information on fuel
switching effect and to distinguish it from energy allocation effect.
5
The three index decomposition methods are multiplicative logarithmic mean Disvisia
index, additive logarithmic mean Disvisia index, and refined Laspeyres index.
6
Copeland and Tailor (1994) propose formal model-based definitions of the three effects.
7
Tibet is dropped from the dataset due to incomplete observations. Chongqing, which
was elevated to a provincial status municipality in 1997, is counted as part of Sichuan
province in this study. Beijing, Shanghai, and Tianjin are provincial-level municipalities
and hence are treated as provinces.
8
The year 1978 is one of the milestones in Chinese economy since then market reforms
started. In addition, the relatively low initial value of capital in 1978 and the high rates of
investment ensure that the estimates of the capital stock for our sample period 1991-2006
are not sensitive to the 1978 benchmark value.
9
Results reported in Table 1 are obtained based on a non-chaining analysis, i.e., using
data on the beginning year 1991 and the ending year 2006.
10
Thanks for one of the anonymous referees of Ecological Economics pointing out this
issue.
115
ACKNOWLEDGEMENTS
I am grateful to Rolf Färe and Shawna Grosskopf for their constructive
comments on an earlier version of this essay. I gratefully acknowledge two anonymous
referees of Ecological Economics whose suggestions have improved the exposition of the
essay substantially.
116
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118
APPENDICES
119
APPENDIX C
Properties of production set:
(1) Null-jointness:  K , L, E, Y, C   S and C  0 imply Y  0 ;
(2) Weak
disposability
of
joint
outputs:
 K , L, E, Y, C   S
and
0   1
imply
Y  Y
imply
 K , L, E,  Y, C   S ;
(3) Strong
disposability
of
good
outputs:
 K , L, E, Y, C   S
and
 K , L, E, Y, C   S ;
(4) Strong disposability of inputs:
 K , L, E, Y, C   S .
 K , L, E, Y, C   S and  K , L, E   K , L, E  imply
120
APPENDIX D
The whole decomposition of index
Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t 
Dy  k t , l t , et , y t ,1; t 
is
(A4.1)
1 24
 D k t 1 , l t 1 , et 1 , y t 1 ,1; t 6  D k t 1 , l t , et 1 , y t 1 ,1; t 2  D k t 1 , l t 1 , et , y t 1 ,1; t 2 
  y 
  y 
 
 y 
 D  k t , l t 1 , et 1 , y t 1 ,1; t    D  k t , l t , et 1 , y t 1 ,1; t    D  k t , l t 1 , et , y t 1 ,1; t   
y
y
y
 
 
 

2
2
2


Dy  k t 1 , l t 1 , et 1 , y t 1 ,1; t    Dy  k t 1 , l t 1 , et 1 , y t ,1; t    Dy  k t 1 , l t , et , y t 1 ,1; t    Dy  k t 1 , l t , et 1 , y t ,1; t  











 

t t 1
t 1
t
  Dy  k t , l t , et , y t 1 ,1; t    Dy  k t , l t , et 1 , y t ,1; t  
Dy  k t , l t , et , y t ,1; t 
D
k
,
l
,
e
,
y
,1;
t


y
 

 
 



2
6
t 1 t 1
t
t
t 1 t
t
t




 Dy  k , l , e , y ,1; t 

D  k , l , e , y ,1; t 
  y

 

t t 1
t
t
t t
t
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  



1 24
 D k t 1 , l t 1 , et 1 , y t 1 ,1; t 6  D k t , l t 1 , et 1 , y t 1 ,1; t  2  D k t 1 , l t 1 , et , y t 1 ,1; t 2 
  y 
  y 
 
 y 
 D  k t 1 , l t , et 1 , y t 1 ,1; t    D  k t , l t , et 1 , y t 1 ,1; t    D  k t 1 , l t , et , y t 1 ,1; t   
  y
  y
 
 y
2
2
2


t 1 t 1
t 1
t
t t 1
t
t 1
t t 1 t 1
t






 Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  

  



 Dy  k t 1 , l t , et 1 , y t ,1; t    Dy  k t , l t , et , y t 1 ,1; t    Dy  k t , l t , et 1, y t ,1; t  
 

 
 



2
6
t 1 t 1
t
t
t t 1
t
t




 Dy  k , l , e , y ,1; t 

Dy  k , l , e , y ,1; t 








t 1 t
t
t
t t
t
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  



1 24
 D k t 1 , l t 1 , et 1 , y t 1 ,1; t 6  D k t , l t 1 , et 1 , y t 1 ,1; t  2  D k t 1 , l t , et 1 , y t 1 ,1; t 2 
  y 
  y 
 
 y 
 D  k t 1 , l t 1 , et , y t 1 ,1; t    D  k t , l t 1 , et , y t 1 ,1; t    D  k t 1 , l t , et , y t 1 ,1; t   
  y
  y
 
 y
2
2
2


t 1 t 1
t 1
t
t t
t 1
t 1
t t 1
t 1
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  

  



t 1 t 1
t
t
t t
t
t 1
t t 1
t
t
 Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  
 

 
 



2
6
t 1 t
t 1
t
t t
t 1
t
  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  








t 1 t
t
t
t t
t
t




D
k
,
l
,
e
,
y
,1;
t
D
k
,
l
,
e
,
y
,1;
t
   y

  y



1 24
 D k t 1 , l t 1 , et 1 , y t 1 ,1; t 6  D k t , l t 1 , et 1 , y t 1 ,1; t  2  D k t 1 , l t , et 1 , y t 1 ,1; t  2 
  y 
  y 
 
 y 
 D  k t 1 , l t 1 , et 1 , y t ,1; t    D  k t , l t 1 , et 1 , y t ,1; t    D  k t 1 , l t , et 1 , y t ,1; t   
  y
  y
 
 y
2
2
2


t 1 t 1
t
t 1
t t
t 1
t 1
t t 1 t
t 1






D
k
,
l
,
e
,
y
,1;
t
D
k
,
l
,
e
,
y
,1;
t
D
k
,
l
,
e
,
y
,1;
t






y


  y
  y

  

t 1 t 1
t
t
t t
t 1
t
t t 1 t
t

 
 

  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  



2
6
  Dy  k t 1 , l t , et , y t 1 ,1; t    Dy  k t , l t , et , y t 1 ,1; t  

 

 

t 1 t
t
t
t t
t
t




  Dy  k , l , e , y ,1; t    Dy  k , l , e , y ,1; t  



: KC  t   LC  t   EC  t   CM  t  .
121
APPENDIX E
Let n be an index for observation, n  1, , N . At each time period t  1, , T , the
production set  Kn , Ln , En , Yn , Cn ; t  is:
S t  
 K

, L , E , Y , C  ; t  :  n 1 znt Ynt  Y
N

(A4.2)

N
z Cnt  C 
t
n 1 n
z K nt  K 
N
t
n 1 n


z Ltn  L
N
t
n 1 n
z Ent  E
N
t
n 1 n
znt  0, n  1,
, N,
where znt ‟s are intensity variables, denoting the weights assigned to each observation.
Superscript t ‟s represent the time period of production technology and superscript  ‟s
denote the time period of production activity. The activity analysis model demonstrates
all technology properties of the Shephard output distance function described in section 2.
Nonnegativeness of intensity variables indicates that production technology exhibits CRS.
Inequality constraints imply freely disposable properties, whereas joint constraints on
good and bad outputs construct weakly disposable property. By imposing the condition

N
n 1
Cnt  0 , the good and bad outputs are null-jointness.
122
APPENDIX F
We conduct a regression with pooled cross-sectional IWGE and WGE 1991-95 (145
observations) and use the result to predict WGE over the time periods 1996-2006. The
predicting equation is estimated:
(A4.3)
WGEn  142.368 1.126 IWGEn   n ,
 72.161
 0.019
where n denotes the provincial-level observations. There is convincing evidence that the
mean of WGE is statistically associated with IWGE with a two-sided p-value <<0.0001.
The adjusted R 2 from this OLS regression is 0.961.
To obtain a conversion factor from WGE to CO2 emissions, we conduct an OLS
regression on the national-level CO2 emissions with a regressor of national-level WGE.
The following equation is estimated:
(A4.4)
Ct  7.118WGE1991:1997,t  5.241WGE1998:2004,t  t ,
 0.247 
 0.111
where WGE1991:1997 is aggregate annual WGE for China if t  1991,1997 and 0 otherwise;
WGE1998:2004 is aggregate annual WGE for China if t  1998, 2004 and 0 otherwise. We
group the sample into two categories as the restructuring of China‟s coal sector in the late
1990s resulted in the closure of thousands of small-scale mines and power plants. There
is overwhelming evidence that the mean of CO2 emissions is associated with WGE
statistically (two-sided p-value << 0.0001). This model explains 93.5% of variations in
national CO2 emissions after adjusting to degrees of freedom.
123
CHAPTER 5
CONCLUSIONS
MAN LI
124
The first essay conducted a national-scale analysis to identify the major drivers
of land use conversions and to assess the relative importance of these drivers in China.
The analysis compiled a unique, national-scale dataset that includes high-quality land use
data, and developed two set of multinomial logit models to analyze land-use choice
among six major uses for two time intervals of 1988-1995 and 1995-2000. The spatial
analysis provides convincing evidence for the existence of spatial dependence. Results
indicate that both socioeconomic and geophysical variables affected land-use change in
China. GDP growth and agricultural investment had larger impacts on farmland
conversion, while population increase and agricultural investment were more influential
in grassland loss. The model is used to evaluate the effectiveness of public agricultural
investment as a policy tool for farmland protection in traditional agricultural regions,
including the Huang-Huai-Hai Plain, Yangtze River Delta, and Sichuan Basin. The
results indicate it is more cost-effective for agricultural investment to larger counties with
lower farmland coverage (<50%) when the objective is to reduce farmland conversion.
The second essay estimated the association between land-use change and SOC
density in China and evaluated the welfare effects of the green growth policy that targets
resource reservation and environment protection in conjunct with soil carbon
sequestration. A statistical SOC density model was developed, which captures spatial
autocorrelation and spatial heterogeneity simultaneously and is easily applied to a large
region. The spatial analysis provides strong evidence for the existence of spatial
nonstationarity in the parameter estimates and spatial autocorrelation in error term.
Results indicate that the SOC density is higher in forest and grass lands and lower in
unused land in China. GDP growth leads to farmland and grassland loss, deforestation,
and idleness, which accelerates soil carbon runoff. Implementation of the green growth
policy could generate up to 0.7-1.1 million Mg SOC and 22.2-37.4 million CNY welfare
losses annually throughout the simulation period (2001-2050). The marginal welfare loss
is approximately ¥15.3/Mg (equivalent to $2.25/Mg) for sequestering about 1 million Mg
SOC per year. The results reveal that soil-based carbon sequestration merits consideration.
125
The third essay presented an alternative approach to index decomposition
method for disaggregating change of CO2 emissions in the context of joint production
settings. Without specifying any functional form for heterogeneous production functions
across regions, it can isolate and quantify the effects of changes in GDP scale, production
technology, technical efficiency, capital-carbon ratio, labor-carbon ratio, energy-carbon
ratio, and GDP-composition by sector. The proposed method was applied to
decomposing province-level changes in CO2 emissions of China into seven components
for the time period 1991-2006. Results indicate that the GDP scale effect was the main
driver behind rising emissions, whereas the emission index associated with capital,
technical change effect, and GDP composition effect were joint contributors to emission
abatement. Capital accumulation played a dominant role across the country. The results
offer informative implications for policymakers to reduce CO2 emissions in China. The
interior regions of China have great potential of technical efficiency improvement by
introducing advanced technology and learning management experiences from coastal
provinces. As the key driver of China‟s overall economic growth, capital accumulation
will still be the most important contributor to abatement across the country in the
foreseeable future. But in the long run, China should rely more on technical progress to
reduce CO2 emissions.
126
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