Types and Institutional Design Principles of
Collaborative Governance in a Strong-government
Society: The Case Study of Desertification Control
in Northern China
Lihua Yang
Associate Professor
School of Public Administration & Workshop for Environmental Governance
and Sustainability Science, Beihang University
Abstract
Although various social actors participate in combating desertification, which has been
widely recognized as a serious ecological and environmental challenge facing modern society,
the types and mechanisms of the participation and collaboration of these actors have received
little attention in the mainstream discourse concerning desertification control and social
governance. Based on a case study of 12 field sites and a meta-analysis of an additional 16
sites reported in the literature on northern China, this study found that the participation of
multiple social actors and their type of collaboration influenced desertification control
performance. This study identified four types of collaboration and determined Type II
(Strong Government with Major Participants) to be the best for desertification control
performance, Type IV (Weak Government without Major Participants) to be the worst, and
Type I (Government Dominance) and Type III (Weak Government with Changed Major
Participants) to be tied for second place. This study proposed eight principles for effective
collaboration that addressed (1) the effective participation of multiple social actors with
enough support resources; (2) open and democratic forums for multiple-actor collaboration;
(3) targeted, organized, systematic, and persistent collaborative activities; (4) effective
mechanisms for discussion, communication, and shared learning; (5) effective trust-building
mechanisms; (6) effective mechanisms of realization and increase of potential gains and fair
distribution of benefits; (7) effective conflict resolution mechanisms; and (8)
experiment-extension governance methods. These findings provide outlines for reforming
the collaboration of multiple social actors in desertification control and in other types of
ecological and environmental governance. This study also provides a theoretical and
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empirical foundation for further research concerning collaborative governance.
Keywords: collaboration, institutions, combating desertification, mechanisms, governance
performance
Introduction
To resolve the dilemmas associated with modern governance, collaborative governance,
which encourages both state and non-state stakeholders to work together to solve complex
social problems (Yang, 2007b; Yang and Lan, 2010) and confront conditions of uncertainty
(Catlaw and Jordan, 2009) by collective decision making and implementation, has been
emphasized by progressively more researchers and practitioners (Agranoff and McGuire,
2003; Allison and Allison, 2004; Ansell and Gash, 2007; Bryson et al., 2006; Connick and
Innes, 2003; Farazmand, 2004, 2006; Freeman, 1997; Friedrichsen, 2006; Gray, 1989;
McGuire, 2006; Healey, 1997; Hudson et al., 1999; O’Leary et al., 2006; Padilla and Daigle,
1998; Reilly, 1998; Sirianni, 2009; Smith, 1998; Walter and Peter, 2000).
Numerous studies
on desertification control have highlighted the important roles of local people and
communities (Reynolds et al., 2007), businesses (Skuras et al., 2000), the government
(Sheehy, 1992), experts and scholars (Yang, 2009, 2010, 2012b; Yang and Wu, 2009, 2010,
2012; Yang et al., 2010), NGOs (non-governmental organizations) (Betsill and Corell, 2008),
international organizations (UNCCD, 1994; Stringer, 2008), and other social actors and
organizations. However, multi-actor collaborative governance has received little attention in
the mainstream discourse concerning desertification control (Yang, 2007b, 2009).
The
existing studies have neglected the concrete types of mechanisms for collaborative
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governance in desertification control and other types of environmental governance.
Furthermore, modern studies of collaborative governance often deem various social actors as
equal bodies (Yang, 2007b) or call on decision makers and practitioners to reduce reliance on
the authority of tradition (Catlaw, 2006) and, as such, cannot deeply study collaborative
governance’s characteristics and mechanisms in strong-government societies (such as
Chinese society) (Yang, 2012b). Thus, most studies on collaborative governance in
strong-government societies simply conclude that the society should be transformed to be
freer and more equal to make collaborative governance among various social actors possible
or claim that real collaborative governance is impossible (Yang, 2009).
However, some studies also found that collaborative governance not only exists in
strong-government societies but is widespread and already plays an important role in
combating desertification in China (Yang, 2009, 2010, 2012a, 2012b; Yang and Wu, 2010,
2012; Yang et al., 2010).
The purpose of this study is to explore the type and institutional
design principles of collaborative governance in a strong-government society, specifically
China.
The research questions are as follows: (1) what are the major types of collaboration
on desertification control in a strong-government society; and (2) what are the major design
principles of successful collaborative governance in a strong-government society?
Research Areas, Methods, and Framework
Research Design and Sites
A two-step study, which combined field and meta-analysis cases, was conducted in 28
sites stretching over six adjacent provinces (Xinjiang, Qinghai, Gansu Ningxia, Shanxi, and
Inner Mongolia) in northern China, which has suffered age-old desertification problems (Fig.
1).
The field study was conducted in 12 sites in three adjacent provinces (Inner Mongolia,
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Gansu, and Ningxia), located at 99°51’E-121°35’E, 36°59’N-49°46N, spanning
approximately 110,248 km2; the meta-analysis was conducted in 16 sites spanning all six
provinces, located at 80°03’-114°07’E, 33 °06’-48°39’N, spanning 226,801 km2.
Among
the 28 sites of this study, there were 24 counties, three cities, and one city district.
Eight
sites were located in arid regions, fifteen in semi-arid regions, one in a transitional zone
between arid and semi-arid regions, and four in transitional zones between semi-arid and
semi-humid regions.
The site populations ranged from 18 thousand (Tianjun) to 3351
thousand (Yulin), the yearly average temperature ranged from -1°C（Xinbaerhuzuo）to 12°C
（Cele），the annual rainfall ranged from 35.5 mm (Cele) to 615.5 mm (Maqu), and the
annual average evaporation ranged from 1379 mm (Guian) to 2751 mm(Cele) (Table 1).
<Insert Fig. 1 around here>
<Insert Table 1 around here>
Data Acquisition
This research combined field studies and meta-analyses.
From June 2006 to May 2012,
this study conducted a field study using multiple methods (including surveys, interviews,
observations, and archive data) in 12 sites spanning three adjacent provinces in northern
China.
In these 12 counties, random sample surveys of 5400 residents were conducted from
March to July in 2011; each country received 450 questionnaires, and the response rate was
86.82% (Table 2a).
Because many farmers and herdsmen cannot read or write, the
questionnaires were distributed to local high schools with students from all over the county,
and the students were asked to help their family or neighbors in rural areas finish the
questionnaires. Face-to-face interviews with 118 participants were conducted from June
2006 to August 2011 (Table 2b).
Each interview was between half an hour and two hours,
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and the interviewees were various social actors such as farmers, scholars or experts,
government officials, and businessmen.
During the same period as the interviews,
participatory and non-participatory observations were collected at each site (Table 2c).
Archive materials such as electronic materials from official websites, published and
non-published literature, governmental gazettes and documents, news articles, county annals,
historical documents, and research reports from 1949 to 2011 were compiled to complement
the field data from the 12 field study cases and the 16 meta-analysis cases.
<Insert Table 2 around here>
Theoretical and Conceptual Background and Framework
Collaboration does not merely mean a method for power brokerage (Fuller, 2009; Kallis
et al., 2009) but crucially refers to co-labor or working together (O’Leary et al., 2006).
Simply recognizing the importance of collaboration in decisions and their implementation
processes is not sufficient; the core issue is “how collaborative processes work” (Kallis et al.,
2009: 637). Agranoff (2006, p. 56) told researchers to ‘‘go beyond heralding the importance
of collaborations to look inside their operations.’’ Researchers have identified several
important procedural attributes for effective collaboration: effective participation of important
social actors as stakeholders (Innes and Booher, 1999; Yang, 2009, 2010, 2012; Yang, et al.,
2010; Yang & Wu, 2010); support of various resources (especially financial, legal,
institutional, policy, technical, information, and moral support) (Fish et al., 2010; Yang & Lan,
2010; Yang, 2009, 2010; Yang et al, 2010;Yang & Wu, 2010); context of collaboration (Fish,
et al., 2010; Yang, 2009; Watson 2004); organization (especially self-organization) and
implementation (especially the implementation of agreements) of collaboration among
various stakeholders (Bouwen and Taillieu, 2004; Bryson et al, 2006; Innes and Booher, 1999;
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Yang, 2009; Yang et al., 2010; Yang & Wu, 2010); productive communication and dialogue,
shared learning, and continuous trust-building mechanisms (Agbodzakey, 2012; Ansell and
Gash, 2007; Innes and Booher, 1999; Bryson et al., 2006; Kallis et al., 2009; Ostrom, 1990;
Yang, 2009, 2010, 2012; Yang et al. 2010; Yang & Wu, 2010); realization, increase, and fair
distribution of potential gains and benefits (Yang, 2009, 2010; Yang & Wu, 2010, 2012);
conflict resolution mechanisms (Ostrom, 1990; Yang, 2009, 2010, 2012; Yang et al. 2010;
Yang & Wu, 2010); and experiment-extension governance methods (Yang, 2009, 2010, 2012;
Yang & Wu, 2010). Based on this theoretical background, we drew a theoretical framework
to study multi-actor collaboration that addresses the types and institutional design principles
of collaboration’s influence on the performance of desertification control. We studied the
types of collaboration from the perspectives of major participants and their participation
levels in different eras (Yang and Li, 2012).
According to Ostrom (1990), institutional
design principles describe essential elements or conditions for robust and sustainable
institutions. For the institutional design principles of collaboration (Yang & Wu, 2010,
2012), we considered the following factors: (1) major participants in collaboration and their
resources and activities; (2) contexts of collaboration; (3) organization and implementation of
collaboration; (4) dialogue, communication, and shared learning mechanisms; (5)
trust-building mechanisms; (6) mechanisms of realization and increase of potential gains and
fair distribution of benefits, (7) conflict resolution mechanisms; and (8) experiment-extension
methods (Fig. 2).
<Insert Fig. 2 near here>
Based on the findings of previous literature (Yang, 2009, 2010, 2012; Yang and Wu,
6
2009, 2010), in this study, we mainly discuss 11 types of social actors: farmers and herders,
families, communities and villages, the public, businesses, the government, scholars, the
media, NGOs (non-governmental organizations), international organizations, and religious
organizations (Fig. 2).
Farmers, herdsmen, families, and communities and villages are the
individuals and units who directly cultivate and take care of the land, and they are key
persons in local desertification control.
The public are rural and urban residents who do not
work directly on desertification control but who join in combating desertification as
volunteers or social participants. Businesses, which make profits, usually take part in
desertification control by holding commercial activities or offering financial and material
support. The government refers to the government departments in charge of desertification
control and land protection such as the Forestry Bureau and the Environmental Protection
Bureau. Scholars are experts, intellectuals, technicians, teachers, and researchers working at
colleges and universities, research institutes, and local desertification control stations who
engage in research on desertification and the environment; they provide specialized
knowledge, skills and practical experience for local desertification control.
The media are
the local and outside newspapers, magazines, TV stations, network media, and so forth.
Generally, the media contribute to desertification control in an indirect way—attracting
public attention and resources by news reports and publicity materials.
NGOs refer to
non-governmental organizations, non-profit organizations, and society intermediary
organizations, whose activities are voluntary and for the benefit of all. NGOs participate in
desertification control mainly by offering volunteer services and material support.
To
distinguish NGOs from international organizations in this study, the term NGOs only refers to
7
domestic organizations such as Friends of Nature and China Foundation for Desertification
Control.
International organizations are the institutions and organizations from overseas,
whose members and activities are international.
They support the activities of combating
desertification by organizing international conferences and providing financial, technical and
human resources (such as Friends of the Earth International). Religious organizations refer
to local religious organizations that are active in desertification control; they often offer
manpower or financial support by encouraging or organizing their followers to join in
desertification control activities.
Variables, Measurements, and Data Analysis
Based on the research questions and the theoretical framework laid-out above, the major
research variables of this study are as follows: (1) the degrees of participation by social actors
in desertification control, (2) the types of collaboration among social actors, (3) the design
principles of collaborative governance, and (4) the performance of desertification control.
To determine the degree of participation by social actors in the 12 field cases, the
questionnaire included a series of multiple-choice questions asking respondents to choose the
major participants they recognized in the 1950s, 1960s, 1970s, 1980s, 1990s and 2000s; we
calculated the percentages of each actor in each decade from the survey responses.
For the
types of collaboration in the 12 field cases, this study classified collaboration into four types
based on the percentages of each actor in each decade from the 1950s to the 2000s (see the
detailed analysis in the “Results” section) and then used the four types to characterize the 16
meta-analysis cases based on the archive data on major participants and their degree of
participation in desertification control.
This study developed design principles based on the
theoretical framework, extensive theoretical analysis, and in-depth analysis of field and
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archive data, and then used these principles to sort the 28 cases into three levels (high, middle,
and low) to make an overall analysis of all the cases, field and non-field.
For the
performance of desertification control in the 12 field cases, this study used a six-point scale,
“very large, large, medium, small, very small, and unknown,” in the questionnaires and then
evaluated performance based on the percentages of survey respondents in each category.
However, because the perceptions of respondents might be different according to the different
contexts of each case, when comparisons were drawn across all 28 cases, the codes (high,
middle, and low) were amended for those counties where enough archive data, interviews,
and observations supported a different performance outcome (Minqin, Dengkou, Duolun,
Wengniute, Aohan, and Naiman).
For the 16 meta-analysis cases, this study coded
desertification control performance into the three levels (high, middle, and low) using only
archive data.
To avoid errors and subjectivity in the coding results, the variables were first coded by a
research assistant, and then the author rechecked all the codes and developed a standard
encoding process and method.
The author then chose six other research assistants, taught
them the encoding process and method, and required them to code all the variables in the
different cases independently.
After the leader of the six assistants integrated and rechecked
all the codes, the author rechecked all the variables.
Next, the author required the six
assistants to repeat the first encoding process by searching more new data and then
rechecking all the codes.
Finally, the author and the six assistants organized a meeting to
code all the variables together (Fig. 3).
The descriptive statistics and correlation analyses
were performed using SPSS (Statistical Product and Service Solutions). Comprehensive
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comparative analysis was used to study the degrees of participation by various social actors
and the types and design principles of collaborative governance in desertification control.
Finally, this study used controlled-comparison, process-tracing (Evera, 1997; George and
Bennett, 2005), and life-story analyses (Plummer, 2001) to clarify the relationship between
the degrees of participation by social actors, types of collaboration, design principles, and the
performance of desertification control.
<Insert Fig. 3 near here>
Results
Degree of Participation by Various Social Actors in Desertification Control
Although the reported amount of participation varied by county, the survey respondents
indicated that all 11 types of social actors participated in desertification control in all 12
counties in each decade from the 1950s to the 2000s (Table 3a). Although the major
participants in different eras were different, the government and farmers and herders were in
the top three in every decade.
Averaging over the 60 years included in this study, from most
to least important, the participants were government, farmers and herders, families,
communities and villages, the public, scholars and experts, businesses, the media, NGOs,
international organizations, and religious organizations.
The government was the most
important participant in each decade except for the 1950s and 1960s when they were edged
out by farmers and herders.
The degree of participation by the government, scholars and
experts, the media, and NGOs continuously increased from the 1950s to the 2000s, while, in
general, the degree of participation by farmers and herders, families, and communities and
villages decreased. The degree of participation by the public and businesses increased up
until the 1990s but decreased in the 2000s.
The degree of participation by religious
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organizations increased slowly between 1950 and 1980, decreased during the 1980s, and then
increased relatively quickly in the 1990s and the 2000s.
The average degree of participation
across the 11 social actors over the past 60 years as rated by the survey respondents was
approximately 10.73%; the average degree of participation increased continuously from 1950
to 2012, except for during the 1970s when it was lower than in the 1960s (Table 3a and Fig.
4a and 4b).
When only the 12 field study cases were considered, although the major participants
were different than those when all 28 cases were included, government, farmers and herders,
and families were still the three most important participants, and international organizations
were still the least important.
The average degree of participation by the 11 social actors in
each of the 12 field study regions was 10.2%.
Six counties had average degrees of
participation below 10.2%, and six counties had averages above 10.2%.
Xilinhot had the
highest average degree of participation of the 12 field study regions at 13.1%, and Dengkou
had the lowest at 8.1% (Table 3b and Fig. 4c).
<Insert Table 3 and Fig. 4 near here>
Relationship between the Overall Participation of Various Social Actors and the Results
of Desertification
Although approximately 58.3% of the 4359 survey respondents in the 12 field study
counties agreed that the degree of desertification was “very serious” (21.7%) or “serious”
(36.6%), approximately 29.9% of the respondents indicated that the performance of
desertification control over the last 60 years was “very high” (8.4%) or “high” (21.5%).
Only 16.3% of the respondents reported that performance was “low” (7.8%) or “very low”
(8.5%).
The correlation coefficients (Pearson) between the degree of participation by
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various social actors and the performance of desertification control (total percentages of
“very high” and “high” by survey respondents) differed across the studied decades.
The
correlation coefficients indicated that the participation of the media, families, communities
and villages, the government, scholars and experts, NGOs, and international organizations
was positively correlated with the performance of desertification control, whereas the
participation of farmers and herders, the public, businesses, and religious organizations was
negatively correlated with performance.
However, only the correlation coefficient for the
media was significant at a 0.05 significance level (Table 4). We also found the correlation
coefficients between the performance of desertification control and the degree of participation
by various types of scholars and experts.
significant at a 0.01 significance level.
Each of these correlation coefficients was
Among the seven types of scholars and experts, the
correlation coefficient was highest for academic communities of the masses and lowest for
colleges and universities (Table 5).
<Insert Tables 4 and 5 near here>
Types of Collaboration of Various Social Actors
Based on the social actors with the highest degrees of participation and their relationship
with the other social actors, we divided the collaboration of various social actors into four
types:
(1) Government dominance (Type I), in which the government played a dominant role
(their degree of participation was over 25% in most of the decades) and was the most
important social actor in desertification control (the degree of participation by most of the
other actors was below 20%). The counties belonging to this type included Zhongwei,
Dengkou, and Wengniute.
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(2) Strong Government with Major Participants (Type II), in which the government still
played a very important role (their degree of participation in over 25% in most of the decades)
and was the most important social actor in desertification control, but there were also times
(the total number in all the decades was over seven) when the degree of participation by some
of the other social actors was over 20%.
Linze, Xilinhaote, and Duolun fall into this type.
(3) Weak Government with Changed Major Participants (Type III), in which the
government played a relatively weak role (their degree of participation was less than 25% in
most of the decades) and was not the most important participant in most of the decades,
rather other major social actors alternated in playing the most important role.
The counties
belonging to this type included Yanchi, Ejin Horo, and Xinzuoqi.
(4) Weak Government without Major Participants (Type IV), in which the government
played a relatively weak role (their degree of participation was less than 25% in most of the
decades) and was not the most important participant in most of the decades, but no other
major social actor played a dominant role either (Fig. 5).
The 16 meta-analysis cases were also categorized using these four types (Table 8).
By
calculating the average performance of desertification control in each the 28 cases, this study
found that Type II collaboration had the highest performance, Type IV had the lowest, and
Types I and III were in the middle (Table 6).
<Insert Fig. 5 and Table 6 near here>
Eight Working Principles for Successful Collaborative Governance
Because the coefficients between the degree of participation by various social actors and the
performance of desertification control were not very significant this study attempted to
determine underlying design principles that can be used to explain the variation in the
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performance of desertification control, in addition to determining types of collaboration.
Based on the theoretical framework (Fig. 2) and repeated data analysis, this study found eight
design principles (Ostrom, 1990; Yang and Wu, 2010, 2012) that were the most important
(Principle 1 has six sub-principles) (Table 7). This study compared all of the 28 cases and
used the principles to characterize them into three relative levels—high, middle, and low—by
dividing the satisfaction degree of the principles (Table 8).
The correlation analysis
(Spearman) between the principles and the performance of desertification control produced
through SPSS showed that the coefficients were all high and significant (Table 7). Thus, at
the 0.05 significance level, this study concluded that these principles of collaborative
governance influenced the performance of desertification control and showed that the more
these principles were followed, the more successful collaborative governance was in
desertification control.
<Insert Tables 7 and 8 near here>
Discussion
Participation of Multiple Actors and Characteristics of Collaborative Governance
The roles of various social actors and their changes in collaborative desertification control
under government domination
The high degree of participation by the government in desertification control and its
continuous participation increase from the 1950s to the 2000s indicated the dominant role of
the government in Chinese society.
Although one might expect that the Chinese
government played the most important role during the 1950s and 1960s because of the
influence of its soviet union system and then played less important roles after the 1980s
because of the policy of reform and opening up, this study found that the government did not
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play the most important role in the 1950s and 1960s and that its role continuously increased
after 1980.
Because the 1950s and 1960s were in the early days of the People’s Republic of
China, people had a great deal of confidence and enthusiasm in the regime, and the
government was relatively democratic. Thus, in these two decades, farmers and herders had
the highest degree of participation in desertification control, although the government’s role
was still important (over 19%). After the policy of reform and opening up, although the role
of other social actors in desertification control increased because of the development of
market and civil society, the government became the most important social actor, and its role
continuously increased through time because the new policy of reform and opening up was
still dominated by the government and desertification control itself required more
government intervention because of its public good nature (Yang, 2009, 2010).
The increase in the roles of scholars and experts, the media, and NGOs from the 1950s to
the 2000s reflected the development of Chinese science and technology and their application
to desertification control after the 1950s (Yang et al. 2013), and the continuous development
of civil society and its diversity, especially after the 1980s.
The relative decrease in the
roles of farmers, families, and communities might be related to the diminishing of the
aforementioned early days effect after the founding of the People’s Republic of China; the
interference with the normal workings of the government during the Great Cultural
Revolution (Yang, 2009); the influence of agricultural cooperatives especially in the 1960s
and 1970s; the return of the government and the development of market and civil society
after 1980; and the negative behaviors of farmers, herders, families, communities, and
villages on desertification control because of the concentration on economic incentives after
15
1980 (Yang, 2009).
The increased roles of the public and businesses in desertification control reflected the
increase in social attention from the 1950s to the 1990s, the developments of Chinese
businesses and their social responsibilities especially after the 1980s, and the participative
governance influence (including citizen participation and cooperate responsibility) especially
after the middle of the 1980s and in the 1990s (Smith 2000; Yang, 2009). However, the
decrease in their roles during the 2000s might be related to the diminishing effect of
participative governance on citizens and businesses, their negative influence on
desertification control (which will be analyzed below), and the exclusion behaviors of other
social actors (such as famers and herders) because of their negative influence on
desertification control (Yang et al. 2010). Our interviews also indicated that this was the
case.
The low degree of participation by religious organizations reflected the destruction of
religious organizations from the 1950s to the 1970s, especially during the Great Cultural
Revolution (Yang, 2009), and the development of markets and more focus on economic
benefits resulting in moral decline after the policy of reform and opening up especially in the
1980s.
The increase in the 1990s and 2000s reflected the new development and recovery of
religious organizations in China.
However, in general, the role of religious organizations
was still limited.
The different degrees of participation by the 11 actors in the 12 field studies shows how
important context was in determining the degree of participation and amount of collaboration.
However, the relatively small differences also showed some consistency among different
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counties; this indicated not only the validity of the surveys by using cross-case data but also
the relative consistency of collaboration among the actors in a relatively centralized society
(Yang, 2012).
Collaborative governance is a co-existence structure of competition and cooperation and a
fluctuating process of different social actors
The results also indicated that although multiple social actors have been involved in
collaborative governance, they often have different statuses and roles crossing areas and
times. Sometimes, groups were involved in governance and one or a few of them were the
dominant actors; sometimes, some actors were involved in governance but the other or others
were the dominant actors.
Thus, different actors fluctuated in the process of the
development of collaborative governance.
Many theorists often define collaborative
governance as a collective decision making process through which various social actors go
beyond their own limited vision of a problem to search for common solutions for the benefit
of society (Agbodzakey, 2012; Bingham and O’Leary, 2008; Bouwen and Taillieu, 2004;
Chrislip and Larson, 1994; Gray, 1989; Healey, 1997).
However, this study’s results
illustrated that major or dominant actors in different areas and decades often changed, and
there was a competitive relationship between various social actors, in addition to their
cooperative relationship in collaborative governance.
The competitive and cooperative
structure among social actors not only showed the complexity of collaborative governance
but also suggested its dynamics, volatility, and diversity.
Although they often only see
different aspects of the problem (Gray, 1989), through collaborative governance, different
social actors with divergent preferences, resources, interests, strengths, and weaknesses, can
work together to pursue their common goals and objectives as well as their own goals and
17
objectives by sharing their information, resources, activities, etc. (Bryson et al., 2006).
Divergent interests and conflicts are inevitable in collaborative governance, and successful
governance often relies on the balance of these different interests, as this study indicated.
Furthermore, many challenges such as turf battles, trust issues, time constraints, delayed
decisions, and the politics of the collaborative process (Agbodzakey, 2012; Aubrey, 1997;
Booher, 2004; Hageman and Bogue, 1998) also influence the competition and cooperation of
various social actors in collaborative governance.
For example, interviewees indicated that
the low level of trust government officials placed in the capability of farmers and herders to
combat desertification influenced the participation of farmers and herders in many counties.
Collaborative governance is a networked, nested, and overlapping multi-actor and
multi-level governance context
This study’s results indicated that collaborative governance involved not only multiple
types of social actors (for example, the government, farmers and herders, communities,
businesses, and experts and scholars including natural scientists, social scientists, research
institutes of Chinese Academy of Sciences, forestry industry systems, academic communities
of the masses, university and colleges, and various desert control and research stations)
(Table 5) but also multiple levels of social actors (for example, the government included
township, county, prefecture, provincial, and national levels; scholars and experts included
local and external scholars at the community, township, county, prefecture, provincial, and
national levels).
Thus, collaborative governance showed a multi-actor, multi-level
management context (Brown, 2002; Peters and Pierre, 2003; Hooghe and Marks, 2003),
within which multiple actors at different hierarchical levels formed a nested and networked
governance structure (Marks, 1993; Ostrom, 1990) and policy making became a complex mix
18
of networks, hierarchies, and markets rather than a single-actor activity (Catlaw, 2008;
Maldonado et al., 2010; Richards and Smith, 2004).
In this nested and networked
governance structure, horizontal competition and cooperation occurred between different
actors and between regions and municipalities, while vertical competition and cooperation
occurred between higher and lower levels of various social actors; together, these horizontal
and vertical components formed a complex and overlapping system.
Collaborative governance is an interactive result of the internal and external factors of its
system; endogenous collaborative governance depends on its self-organizing capacity,
while exogenous collaborative governance depends on the support of external forces
Collaborative governance is also influenced by the factors inside and outside of its
system (Table 7).
For example, if we deem that a county is a relatively independent system,
all the factors within the county influencing the collaborative governance (e.g., the
participants and their preferences, authority, resources, and status of representatives within
the county) (Purdy, 2012; Yang, 2007b) form the internal system, while all the factors outside
of the county influencing the collaborative governance (e.g., the external scholars, experts,
and NGOs and their preferences, authority, resources, and status of representatives outside of
the county) (Yang, 2007b) compose the external system.
Collaborative governance is an
interactive process and result of its internal and external systems.
This internal/external split
also relates to the nested and networked structure of collaborative governance. Because the
role of the different hierarchical levels of government (e.g., village, township, county,
prefecture, provincial, regional, and national) in collaborative governance are “enmeshed in
territorially overarching policy networks” (Marks, 1993: 403), low-level governance is often
influenced
by
high-level
governance
especially
in
strong-government
societies.
Furthermore, high-level governance is also influenced and restricted by low-level governance
19
because many of the decisions and policies of the high-level must be implemented by the
low-level, although the high-level can often supervise the low-level.
If a system has
relatively high independence, its collaborative governance is mainly influenced by internal
factors, while if a system has relatively low independence, its collaborative governance is
strongly influenced by external factors.
Sometimes, the external factors are important
support factors of collaborative governance (Campbell, 1992; Yang, 2009, 2010; Yang et al.,
2010; Yang and Wu, 2009, 2010). Thus, the performance of collaborative governance is
also the interactive result of internal and external factors across system boundaries. For
instance in this study, many desert control stations, as both the local research and desert
control institute and the research base of CAS or another national research institute, played
the role of boundary organizations which connected local scholars, external scholars, and
many other social actors together and provided institutionalized forums for stakeholders to
share knowledge and “work together to bridge the gaps between disparate frames and
viewpoints” (Kallis et al., 2009: 637).
This study also indicated that successful collaborative governance can be roughly divided
into two types: endogenous and exogenous. The former is an independent self-governing
system, and its success often depends on its own self-governing capability, while latter’s
success often depends on the support of its external forces rather than its own capacity.
In
general, when the internal forces are strong, the result of collaborative governance is often
determined by both the internal and external forces, but the internal forces play more
important roles.
When the internal forces are weak or the internal capability of
self-governance is low and there are strong external forces, the result of collaborative
20
governance is often dominated by the external forces.
When both the internal
self-governing capability and the external support forces are weak, the system of
collaborative governance is in chaos.
In a more democratic society, the success of
collaborative governance depends primarily on its self-organizing capability; while in a
strong-government society, the success of collaborative governance depends on the
organizing capacity and openness of the government.
In a strong-government society, when
a system has enough self-organizing capability and the government respects or does not
destroy its capability, or the system can resist the intervention of the government,
collaborative governance has relatively high performance, and this can be deemed as
successful endogenous collaborative governance.
When there is not enough self-governing
capability but the government or other external actors or organizations (e.g., NGOs and
international organizations) can support collaborative governance, collaboration can achieve
relatively high performance, and this can be deemed as successful exogenous collaborative
governance; however, if the government or other external forces cannot support the system,
there will usually be low performance.
Complex relationships between the participation of social actors and the performance of
desertification control
The low significance of correlation coefficients between the 11 social actors, except the
media, and the performance of desertification control and the changes of the coefficients
across decades might illustrate two problems: (1) the performance of desertification control
was not only correlated with the degree of participation by various social actors but also with
other factors which should be further studied; (2) there is still much room for improving the
roles of these actors in desertification control.
Furthermore, the following aspects of the
21
correlation coefficients must be addressed:
(1) The high value and significance of the coefficient for the media indicated the
important role of the media in desertification control, which was ignored by earlier studies
and practitioners (Yang, 2009; Yang et al., 2010). This also suggested that the role of the
media in Chinese desertification control should be expanded.
(2) The negative coefficients for businesses and the public were consistent with the
perceptions of interviewees who indicated that businesses deteriorated desertification
conditions by focusing more on economic development and pursuing economic benefits
through so-called environmental protection activities, while the public, though often highly
enthused, deteriorated conditions through unscientific desertification control activities
because of their lack of related knowledge, skills, and experience.
This finding was
consistent with previous studies (Yang, 2009; Yang et al., 2010).
(3) The negative coefficient for farmers and herders might be related to their
over-cultivation, overgrazing, over-deforestation, and excessive firewood collection (Chao,
1984; Zhu, 1989; Zhu, Liu, & Di, 1988).
(4) The negative coefficient for religious organizations might be related to the
destruction of religious organizations between 1950 and 1980, especially during the Great
Cultural Revolution (Yang, 2009). It could also be related to religious organizations’
economic, rather than moral, activities, especially in the 1990s, although their degree of
participation increased at that time.
(5) The high and significant coefficients for the seven types of scholars and experts
illustrated the importance of scholar/expert participation in desertification control and
22
suggests that they should be incorporated in other collective action dilemmas (Yang,2007a,
2009, 2010, 2012; Yang and Wu, 2009, 2010, 2012; Yang et al., 2010, 2013). Academic
communities of the masses had the highest and most significant coefficient among scholars
and experts indicating their important role in desertification control, which should be
expanded in future activities for combating desertification.
Colleges and universities had
the lowest and least significant coefficient illustrating their relatively less importance role as
external organizations or scholars; they often did not have enough local knowledge or social
capital with local people to effectively participate in desertification control (Yang, 2009; Yang
et al., 2010). The relatively high and significant coefficients for natural scientists, research
institutions of the Chinese Academy of Sciences, research institutions in forestry industry,
anti-desertification research bases, and social scientists indicated that the participation of
scholars and experts was a new type of collaborative governance that encouraged cooperation
among various types of scholars, experts, and their organizations (Yang, 2009, 2012; Yang et
al., 2010).
The four collaboration types provide a possible type framework for analyzing
collaborative governance in a strong-government society
Understanding types of collaborative governance is a necessary step for improving its
performance (Yang, 2007b).
This study indicated that in addition to the categories of
horizontal and vertical collaboration (Maldonado et al., 2010; McGuire, 2006) and
endogenous and exogenous collaboration, collaborative governance in a strong-government
society such as China could be divided into four types by analyzing the relationship among
various social actors (Yang, 2012). These types provide a theoretical and empirical map for
analyzing collaborative governance and concrete instructions for policy making in China and
23
other strong-government societies.
They could also be the references for analyzing
collaboration in other societies, especially for those that want to improve the role of the
government in collaborative governance and for many developing counties. Although many
theorists have speculated that collaboration in which social actors share equal power is more
democratic or more like a real collaboration, in many developing societies and societies with
strong-government traditions, the government often plays a more important role than any
other actor. Understanding this strong-government context and the types of collaboration
that occur in this context is important for analyzing collaboration and improving performance.
Thus, the four types of collaborative governance we identified not only have significant
theoretical importance in understanding the whole spectrum of collaborative governance but
also have significant empirical importance in making better policies and improving
performance of collaborative governance in many societies.
The different performance
levels of the four types of collaborative governance (e.g., the type of Government domination
with major social actors had the highest governance performance, while the type of Weak
government without major social actors had the lowest) provide concrete instructions for
policy makers and practitioners to design, change, and improve their policy and decisions.
The eight design principles provide concrete instructions for analyzing and building
successful multi-collaborative governance in a strong-government society
The study of factors or principles influencing collaborative governance is an ongoing
debate that requires both theorists and practitioners to understand how collaborative
governance should be organized. Researchers (e.g., Bryson et al, 2006; Innes and Booher,
1999) have identified some important procedural attributes for effective collaboration
including “the presence of shared practical tasks; initial agreements; a reliance on
24
self-organization rather than an externally imposed structure; the use of high-quality, agreed
upon information sources; proceeding with agreements when there is overwhelming support;
external legitimacy of the process; resources and commitment to equalize power differences
between participants; continuous trust-building activities, and genuine engagement in
productive dialogue” (Kallis et al. 2009, 637). Sirianni (2009) also suggested that
collaborative governance follow eight core principles: (1) stress citizens as “co-producers of
public goods rather than as clients or needy subjects”; (2) encourage “communities to
recognize and mobilize their own problem-solving assents”; (3) broadly share “professional
expertise” and be “more receptive to local knowledge”; (4) promote “serious and widespread
public dialogue”; (5) promote “relationships among citizens, civil associations, and public
agencies”; (6) organize strategic coordination of “the resources needed to build fields and
networks for accomplishing public tasks”; (7) transform “institutional cultures to work with
citizens and serve as educational resources”; (8) implement “widespread accountability for
problem solving both inside and outside government” (Dzur, 2010: 44).
The possible
correlation between adherence to the eight design principles and the performance of
desertification control suggests that these principles are important factors for developing a
robust collaborative governance system in a strong government society.
These principles
heavily stressed the relationships of various social actors in collaborative governance.
These principles might not cover all the rules or principles for designing a robust
collaborative governance system, but they include some of the most important elements.
They provide us with a new framework to analyze collaboration in desertification control, to
improve desertification control performance, to transform fragile and failing desertification
25
control systems into more robust and successful systems, and to design new robust
collaborative governance institutions and systems.
Acknowledgements
This study was supported by the National Natural Science Foundation of China (71073008).
I would like to thank Professor Zhiyong Lan for comments and suggestions on an earlier
version of the paper.
I would also like to thank my graduated students and research
assistants Wensheng Chen, Teng Zhang, Pengyun Shen, Qing Xia, Yun Zhang, Chen Li,
Yuzeng He, Mulan Hao, Xiaojing Ni, and Yao Xue for their help with fieldwork and data
collection.
I want to especially thank Lu Tang for her hard work on collecting data for
meta-analysis cases and early analyses in Chinese.
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Tables and Figures
Figures
Fig.1. Distribution of the 12 field study sites and 16 meta-anlysis sites
Resource: http://www.landong.com/ps_sctx_125713.htm.
35
Fig.2. The theoretical framework for analyzing collaborative governance in
desertification control
36
Fig. 3. The framework for coding variables
37
a. The line chart for different decades
b. The area chart for different decades
100%
40
Farmers & herders
35
30
Families
90%
Communities &
villages
80%
Religious
organizations
70%
The media
60%
Scholars &
experts
NGOs
Normal residents
Businesses
25
Percentages
International
organizations
Government
20
Scholars & experts
50%
The media
40%
Religious
organizations
30%
15
10
Government
Businesses
Normal
residents
Communities &
villages
NGOs
20%
International
organizations
5
Families
10%
0
1950s
1960s
1970s
1980s
1990s
2000s
0%
1950s
Farmers &
herders
1960s
1970s
1980s
1990s
2000s
c. The area chart for different counties
100%
Average
90%
International organizations
80%
NGOs (P1.4)
70%
Religious organizations
The Media(p.1.5)
60%
50%
40%
Schoalrs & experts (p1.3)
Government (P1.1)
Businesses
The Public
30%
Communities & villages (P.1.2)
20%
Families
10%
Farmers & herders
0%
Fig.4. Major participants in different decades from the 1950s to the 2000s as reported by the survey
respondents in the 12 field study counties (2011)
38
Wenniute
60
60
50
50
50
40
40
40
30
Percentages
Dengkou
60
Percentages
Percentages
a. Type I: Government Dominance
Zhongwei
30
20
20
20
10
10
10
0
0
1950s
1960s
1970s
1980s
1990s
0
1950s
2000s
1960s
1970s
1980s
1990s
2000s
60
50
50
50
40
40
40
Percentages
60
30
30
20
20
10
10
10
0
1950s
1960s
1970s
1980s
1990s
2000s
1960s
1970s
1980s
1990s
2000s
50
50
50
40
40
40
Percentages
60
Percentages
Percentages
60
30
20
20
10
10
10
1970s
1980s
1990s
2000s
1950s
1960s
1970s
1980s
1990s
2000s
d. Type IV: Weak Government without Major Participants
Minqin
Aohan
50
50
50
40
40
40
Percentages
60
Percentages
60
30
20
20
10
10
10
Fig. 5.
1960s
1970s
1980s
1990s
2000s
1970s
1980s
1990s
2000s
1950s
1960s
1970s
1980s
1990s
1950s
1960s
1970s
1980s
1990s
2000s
0
0
1950s
1960s
30
20
0
1950s
Naiman
60
30
2000s
0
0
1960s
1990s
30
20
1950s
1980s
Xinzuoqi
60
0
1970s
0
1950s
c. Type III: Weak Government with Changed Major Participants
Yanchi
Ejin Horo
30
1960s
30
20
0
1950s
Duolun
60
Percentages
Percentages
b. Type II: Strong Government with Major Participants
Linze
Xilinhaote
Percentages
30
1950s
1960s
1970s
1980s
1990s
2000s
2000s
Four types of collaboration among social actors in desertification control as reported by the survey respondents in the
12 field study counties (2011)
Note:
Farmers & herders;
Scholars & experts;
The media;
Families;
Communities & villages;
Religious Organizations;
NGOs;
The public;
Businesses;
International organizations;
39
Government;
Governance performance.
Tables
Table 1
Characteristics of the 28 study sites
Provinces
Longitude
Latitude
Climate division
Total area
Annual
Annual
Annual
(ten thousand) （km²）
Population
average
average
average
temperature precipitation evaporation
(°C)
Cases
（mm）
(mm）
a. The 12 field study cases
Linze County (2001)a
Gansu
99°51'-100°30'E
38°57'-39°42'N
Arid
14.6
2777
7.7
115
2212
Minqin County (1994)
Gansu
101°49'-104°12'E 38°03'-39°27'N
Arid
27.4
15900
8.3
127.7
2644
Zhongwei County (1995)
Ningxia 104°17'-105°37'E 36°59'-37°42'N
Arid～semi-arid
27
4671
8.4
186
1914
Yanchi County (2004)
Ningxia 106°30'-107°47'E 37°04'-38°10'N
Semi-arid
16.5
8661
7.8
300
＞2000
Dengkou County (1998)
IM b
106°09'-107°10'E 40°09'-40°57'N
Arid
12
4167
7.6
143.9
2327
Ejin Horo Banner (2011)
IM
108°58'-110°25'E 38°56'-39°49'N
Semi-arid
14
5600
5.7
380
2563
Xilinhot City (2004)
IM
115°13'-117°06'E 43°02'-44°52'N
Semi-arid
25.2
14785
1.6
250-350
1746
Duolun County (2000)
IM
115°51'-116°54'E 41°46'-42°36'N
Semi-arid～semi-humid
10.5
3773
1.6
385
1714
Wengniute Banner (1993)
IM
117°49'-120°43'E 42°26'-43°25'N
Semi-arid～semi-humid
47.3
11882
0-7
370
2106
Xinbaerhuzuo Banner(2007)
IM
118°33'-112°05'E 47°19'-49°46'N
Semi-arid
4.2
21600
-1
150-250
Aohan Banner (1990)
IM
119°30'-120°53'E 41°42'-43°02'N
Semi-arid～semi-humid
60
8294
5-7
310-460
2162
Naiman Banner (2001)
IM
120°19'-121°35'E 42°14'-43°32'N
Semi-arid～semi-humid
43.2
8138
6-6.5
366
2000
15.5
32430
12
35.5
2751
-
b. The 16 meta-analysis cases
Cele County (2011)
Xinjiang
80°03'-82°10'E
35°18’-39°30’N
Arid
Qiemo County (2003)
Xinjiang
83°25'-87°30'E
35°40’-40°10’N
Arid
5.7
14025
10
18.6
2507
Qitai County (1995)
Xinjiang
89°13'-91°22'E
43°25’-45°29’N
Arid
22.1
20065
6.5
168
2141
Tianjun County (2003)
Qinghai
96°49'-99°41'E
36°53’-48°39’N
Semi-arid
1.8
25700
1.5
360
2504
Guinan County (2000)
Qinghai 100°13'-101°33'E 35°09’-36°08’N
Semi-arid
6.8
6650
2.3
403.8
1379
Hanyan County (2005)
Qinghai 100°23-101°20'E 36°44’-37°39’N
Semi-arid
3.8
4853
1.5
400
1581
1650
Sunan County (2004)
Gansu
97°20'-102°13'E
37°28’-39°49’N
Semi-arid
4
20456
0-3
350
Maqu County (2006)
Gansu
100°46'-102°29'E 33°06’-34°33’N
Semi-arid
4.5
10190
1.1
615.5
Liangzhou District (2005)
Gansu
102°02'-103°23'E 36°29’-39°27’N
Semi-arid
101
5080
7.7
100
2020
Arid
23
4639
8.8
206
1933
-
Lingwu City (2006)
Ningxia 106°20'-113°00'E 40°10’-43°22’N
Pingluo County (2005)
Ningxia 106°32'-106°54'E 38°54’-38°91’N
Arid
30
2086
2.8
334
1755
Wuqi County (2002)
Shaanxi 107°38'-108°32'E 36°33’-37°24’N
Semi-arid
12
3791
7.8
483.4
1565
Yulin City (2011)
Shaanxi 108°65'-110°02'E 37°22’-38°74’N
Semi-arid
335.1
42920
8.3
365.7
-
Siziwang Banner (2004)
IM
110°20'-113°00'E 40°10’-43°22’N
Semi-arid
20.9
25513
1-6
300
2000
Wuchuan County (2009)
IM
110°31'-111°53'E 40°47’-41°23’N
Semi-arid
17.3
4885
3
360
2055
Xinghe County (2004)
IM
113°21'-114°07'E 40°26’-41°27’N
Semi-arid
30
3518
4.2
409.4
2037
Note:
a
The year of sources.
b
Inner Mongolia
Source: Yang et al., 2013; government websites of the study sites and county annals.
40
Table 2
Survey and interview distribution in the 12 cases in northern China (2006-2011)
Areas LinzeMinqinZhongweiYanchiDengkouEjin HoroXilinhotDuolunWengniuteXinbaerhuzuoAohanNaimanTotal
a. Interview distribution
Farmers & residents
Scholars, experts &
technicians
Government officials
Businessmen
Religious groups & NGOs
Total
4
3
6
11
5
4
1
4
1
2
2
3
1
4
1
0
1
2
2
0
1
4
1
5
26
42
1
0
0
8
11
0
1
29
1
0
0
10
3
0
0
8
6
0
0
9
3
0
0
8
4
0
0
9
3
0
0
4
5
2
0
10
3
0
0
5
4
0
0
9
1
2
0
9
45
4
1
118
450
100
418
450
80.00
345
450
450
99.56 72.00
439
304
450
38.89
150
450
93.56
342
450
100
449
460
100
458
450
86.00
387
450
100
362
92.89
95.83
97.99 93.83
85.71
81.23 99.78
99.57
100
11
7
2
2
b. Survey distribution
Number of copies sent
450
Response rate (%)
75.78
Number of valid responses
328
Valid rate among responses
96.19
(%)
c. Observation distribution
Numbers
4
2
9
2
2
a
“Types of organization” refers to the people in these organizations.
b
Numbers in brackets are the percentages of valid responses.
3
450 5410
96.00 86.82
424 4406
80.44 98.15 93.78
3
5
52
Sources: Yang et al., 2013 and Yang and Li, 2012.
41
Table 3
Major participants in desertification control in different decades and counties from the 1950s to the 2000s
as rated by the survey respondents in the 12 cases in northern China (2011).
Farmers FamiliesCommunitiesThe publicBusinessesGovernmentScholars The MediaReligious
& herders(%)
(%)
& villages
(%)
(%)
(%)
(%)
& experts (%)
(%)
NGOs International Average
organizations(%)2
(%)
Organizations
(%)
a. In different eras
The 1950s
39[1]a
14.1[4]
16.8[3]
10.1[5]
3[7]
19.2[2]
4[6]
0.8[10]
0.7[11]
1.5[9]
1.6[8]
10.07[6]
The 1960s
28.1[1] 22.1[2]
14.5[4]
12.8[5]
4.8[7]
20[3]
4.9[6]
1.8[8]
0.8[11]
1.9[9]
1.2[10]
10.26[4]
The 1970s
18.7[2] 18.5[4]
18.7[2]
14.4[5]
5.7[6]
22.7[1]
5.6[7]
2.1[8]
1.8[10]
2[9]
1.2[11]
10.13[5]
The 1980s
15.9[3] 13.7[5]
14.8[4]
21.2[2]
8.9[7]
24.9[1]
9.2[6]
3.4[8]
1.3[11]
2.4[9]
1.2[10]
10.63[3]
The 1990s
14.5[3]
12.7[4]
14.7[2]
12[5]
28.6[1]
11.5[7]
6.6[8]
2.3[11]
3.5[9]
2.4[10]
10.98[2]
The 2000s
17.3[2] 12.4[5]
11.2[6]
13.6[4]
11[7]
35[1]
13.9[3]
7.2[8]
2.5[11]
6.5[9]
4.2[10]
12.25[1]
Average
22.3[2] 15.5[3]
14.8[4]
14.5[5]
7.5[7]
25.1[1]
8.2[6]
3.7[8]
1.6[11]
2.9[9]
1.9[10]
10.73
12[5]
b. In different counties
a
Linze
22.5[2] 10.7[6]
20.7[3]
16.9[4]
6.1[7]
35.7[1]
16.7[5]
4.9[8]
0.7[11]
1.2[10]
2.5[9]
12.6[2]
Minqin
22.7[1] 10.7[5]
19[2]
15.3[4]
3.9[7]
18.6[3]
4.8[6]
1.5[9]
1.1[11]
2[8]
1.3[10]
9.17[8]
Zhongwei
16.8[2] 12.4[5]
16.1[3]
14.5[4]
8.6[7]
31.2[1]
12.2[6]
4.3[8]
1.5[11]
2.5[9]
2[10]
11.1[4]
Yanchi
15.9[4]
14[5]
16.5[3]
17[2]
13.1[6]
21[1]
5.8[7]
3[8]
1.7[11]
2.8[9]
2.3[10]
10.28[6]
Dengkou
16.9[2]
4.4[7]
7.2[5]
7.7[4]
5.8[6]
31.9[1]
14.1[3]
1[10]
1[10]
1.2[8]
1.2[8]
8.4[12]
Ejin Horo
17.8[1]
14[3]
14.6[2]
12.6[4]
2.2[10]
10.1[5]
7.5[6]
4.2[8]
2.3[9]
6.2[7]
1.5[11]
8.45[11]
Xilinhot
25.9[2] 15.7[3]
11.7[5]
11.7[5]
9.5[7]
40.2[1]
15.7[3]
5.7[8]
0.7[11]
3.4[10]
3.9[9]
13.1[1]
Duolun
36.1[1] 18.8[3]
10.1[5]
11.2[4]
4.9[7]
32.4[2]
5.3[6]
2.2[8]
1.1[10]
1.4[9]
1.1[10]
11.33[3]
Wengniute
13.3[3] 18.6[2]
12.8[4]
10.6[5]
7.4[6]
29.2[1]
4.2[7]
1.9[8]
0.3[11]
1[9]
0.9[10]
9.11[9]
Xinbaerhuzuo 16.8[2] 21.2[1]
14.9[3]
13.3[5]
3.8[8]
14.8[4]
2[9]
7.5[6]
1.2[10]
6.7[7]
1.1[11]
9.39[7]
Aaohan
11.7[5]
15.7[4]
16[3]
6[6]
19[2]
4.9[7]
2[8]
0.8[10]
1.6[9]
0.5[11]
9.11[9]
Naiman
21.9[1] 19.6[2]
12.2[5]
19.6[2]
8.1[6]
14.8[4]
7.2[7]
3.9[10]
4.3[9]
4.8[8]
3.7[11]
10.92[5]
Average
20.72[2] 14.32[3] 14.29[4]
13.87[5]
6.62[7]
24.91[1]
8.37[6]
3.51[8]
1.39[11]
2.9[9]
1.833[10]
10.25
22[1]
[1] to [12]refers to the rank
42
Table 4
Correlation coefficients (Pearson) between the participation of various social actors and the performance of
desertification control as reported by the survey respondents in the 12 cases in northern China from the 1950s to
the 2000s (2011)
Coefficients &
Farmers & Families Communities &
Eras
significance
herders
The 1950s
Coefficient
-0.453
Significance
The 1960s
The 1970s
The 1980s
The 1990s
The 2000s
Total
The BusinessesGovernment Scholars &
The
Religious
experts
Media
organizations
0.118
-0.122
-0.031
-0.089
-0.077
-0.250
0.926
0.716
0.706
0.293
0.784
0.227
0.433
villages
Public
0.145
0.528
-0.261
0.030
0.139
0.653
0.078
0.413
NGOs
International
organizations
Coefficient
-0.077
-0.145
-0.063
-0.047
-0.330
0.066
0.229
-0.048
-0.152
-0.238
-0.049
Significance
0.811
0.652
0.845
0.885
0.295
0.839
0.475
0.883
0.638
0.457
0.879
Coefficient
0.310
0.304
-0.543
-0.100
-0.306
0.129
0.115
-0.113
-0.186
-0.045
0.330
Significance
0.327
0.336
0.068
0.758
0.334
0.690
0.722
0.727
0.563
0.889
0.296
Coefficient
0.154
0.328
0.213
0.341
-0.029
0.168
0.272
0.318
0.078
-0.152
0.052
Significance
0.632
0.298
0.507
0.278
0.930
0.603
0.393
0.314
0.810
0.636
0.873
Coefficient
0.054
-0.015
-0.156
-0.097
-0.186
0.242
0.228
0.749**
-0.340
0.168
0.127
Significance
0.867
0.962
0.628
0.765
0.562
0.449
0.447
0.005
0.279
0.603
0.694
*
0.210
0.622
*
0.072
Coefficient
0.084
-0.167
0.162
0.026
0.303
0.353
0.328
0.648
Significance
0.795
0.603
0.616
0.936
0.338
0.260
0.298
0.023
0.513
0.031
0.823
Coefficient
-0.009
0.166
0.174
-0.021
-0.062
0.198
0.243
0.736**
-0.126
0.315
0.085
Significance
0.977
0.606
0.588
0.948
0.849
0.538
0.446
0.006
0.697
0.318
0.792
Note：*P < 0.05(two-tailed)；**P < 0.01(two-tailed).
43
Table 5
Correlation coefficients (Pearson) between different types of scholars and experts and the performance
of desertification control as reported by survey respondents in the 12 field study cases over the past 60
years (2011)
Natural Research institutions of the Research InstitutionsAcademic communities Colleges & Anti-desertificationSocial scientists
scientists Chinese Academy of Sciences in forestry industry
of the masses
universities research bases
Coefficients 0.688*
0.698*
0.659*
0.865**
0.52
0.706*
0.647*
Significance 0.013
0.012
0.020
0.000
0.081
0.010
0.023
Note：*P < 0.05(two-tailed)；**P < 0.01(two-tailed).
44
Table 6
The relationship between the type of collaborative governance
and the performance of desertification control for all 28 cases
Type I
Frequencies
Average scores of the performance
of desertification control
Ranks
Type II
Type III
Type IV
7
8
7
6
2.14
2.75
2.14
1.67
[2]
[1]
[2]
[4]
Note: Given H=3, M=2, L=1 in Table 8; H=High, M=Middle, and L=Low.
45
Table 7
Eight design principles for successful collaborative governance in a strong government society and the
coefficients (Spearman) of the performance of desertification control in the 28 cases in northern China
Eight design principles
P1. There is effective participation of multiple social actors with enough support of resources
P1.1. There is active organization and coordination by the government with policy, organization, institutional,
material, and financial support.
P1.2. There is enough collaboration willingness and ability of farmers, herders, families, and communities as local
actors.
P1.3. There is enough research and technical support by scholars.
P1.4. There is active participation by NGOs with human resources and financial support.
P1.5. There is active participation by the media, which improves social concerns and provides material and financial
support.
P1.6. There is active participation by other social actors with human resources, material, and knowledge support.
P2. There are open and democratic forums for multiple-actor collaboration.
P3. Collaborative activities are targeted, organized, systematic, and persistent.
P4. There are effective mechanisms for discussion, communication, and shared learning.
P5. There are effective trust-building mechanisms.
P6. There are effective mechanisms of realization and increase of potential gains and fair distribution of
benefits
P7. There are effective conflict resolution mechanisms
P8. Collaborative activities use experiment-extension governance methods
Coefficients
(significance)
0.778**
(0.000)
0.672**
(0.000)
0.613**
(0.001)
0.429*
(0.023)
0.570*
(0.002)
0.639**
(0.000)
0.474*
(0.011)
0.643**
(0.000)
0.962**
(0.000)
0.717**
(0.000)
0.750**
(0.000)
0.679**
(0.000)
0.521**
(0.005)
0.539**
(0.003)
Note：*P < 0.05(two-tailed)；**P < 0.01(two-tailed).
46
Table 8
Eight design principles and collaborative type classification of the 28 cases and their
performance in desertification control
Cases
Provinces
Collaboration
Types
Eight Principles for Successful Collaborative Governance
P1
P2
P1.1
P1.2
P1.3
P1.4
P1.5
P3
P4
Performance
P5
P6
P7
P8
P1.6
a. The 12field study cases
1.Linze
Gansu
Type II
H
H
M
H
M
M
H
H
H
H
H
M
M
H
H
2.Minqin
Gansu
Type IV
M
H
M
L
M
H
M
M
M
M
M
M
M
M
M
3.Zhongwei
Ningxia
Type I
M
H
M
H
M
M
M
M
H
H
H
H
M
H
H
4.Yanchi
Ningxia
Type III
M
H
L
L
M
L
H
M
M
M
M
M
M
M
M
5.Dengkou
Inner Mongolia Type I
L
M
L
L
L
M
M
M
M
H
M
M
M
H
M
6.Ejin Horo
Inner Mongolia Type III
M
H
M
M
M
L
M
L
M
M
M
H
M
H
M
7.Xilinhot
Inner Mongolia Type II
H
H
M
H
H
M
M
L
L
M
M
H
H
M
M
8.Duolun
Inner Mongolia Type II
H
H
H
M
M
M
M
L
H
H
H
H
M
H
H
9.Wengniute
Inner Mongolia Type I
M
M
M
M
L
M
L
M
M
H
H
Ｈ
Ｍ
Ｈ
M
10.Xinbaerhuzuo
Inner Mongolia Type III
H
H
M
L
H
H
L
H
H
Ｈ
Ｈ
Ｈ
Ｍ
Ｍ
H
11.Aohan
Inner Mongolia Type IV
H
H
H
H
M
H
H
H
H
Ｈ
Ｍ
Ｈ
Ｈ
Ｈ
H
12.Naiman
Inner Mongolia Type IV
M
M
M
M
M
M
M
M
M
Ｍ
Ｍ
Ｈ
Ｈ
Ｈ
M
H
H
H
H
H
H
H
M
L
M
M
b. The 16 meta-analysis cases
13.Cele
Xinjiang
Type II
H
H
M
H
M
M
H
M
H
H
H
14.Qiemo
Xinjiang
Type II
H
H
H
L
H
M
M
M
H
M
M H
15.Qitai
Xinjiang
Type III
M
M
H
L
M
L
L
L
M
M
M
16.Tianjun
Qinghai
Type IV
L
L
L
L
L
L
L
L
L
L
L
M
L
M
L
17.Guinan
Qinghai
Type III
M
M
M
M
M
L
M
M
H
M
M
Ｍ
L
L
M
18.Haiyan
Qinghai
Type II
H
H
M
H
M
M
L
M
M
M
M
M
L
M
M
19.Sunan
Gansu
Type III
M
M
M
H
M
L
M
L
L
Ｍ
Ｍ
M
M
M
M
20.Maqu
Gansu
Type IV
L
L
L
M
L
L
L
L
M
Ｌ
L
L
L
M
L
21.Liangzhouqu
Gansu
Type I
M
H
M
M
M
L
H
M
M
M
M
M
H
H
M
22.Lingwu
Ningxia
Type II
H
H
M
H
M
M
H
H
M
H
Ｈ
H
M
H
H
23.Pingluo
Ningxia
Type I
H
H
M
M
M
M
H
M
H
M
M
M
M
H
M
24.Wuqi
Shaanxi
Type II
H
H
M
H
M
M
M
H
H
M
M
H
M
H
H
25.Yulin
Shaanxi
Type I
M
H
M
M
M
L
M
M
M
M
M
H
M
H
M
26.Siziwang
Inner Mongolia Type I
M
H
M
M
L
L
M
L
M
M
L
M
L
M
M
27.Wuchuang
Inner Mongolia Type III
M
M
M
M
M
L
L
L
M
L
L
M
M
M
M
28.Xinghe
Inner Mongolia Type IV
L
L
L
L
L
L
L
L
L
M
Ｌ
L
L
M
L
Note: H=High; M=Middle; L=Low.
47