This is my own Model for your approval. The following research paper below is base of this
model.
There are some title of research paper design with the help of Sohail Ahmad Sahib after long
discussion on Friday.
1. From Green Financing to Green Economy: Unleashing the Impact of Green Finance on
AQI in Top Polluted Countries
2. The Impact of Green Financing on Air Quality Index in Top Polluted Countries: A MultiVariable Analysis
3. Green Financing and Air Quality in Top Polluted Countries: Exploring the Mediating
Role of Renewable Energy Adoption
4. Green Financing, Economic Development, and Air Quality in Top Polluted Countries:
An Instrumental Variable Approach
5. Government Commitment, Green Financing, and Air Quality in Top Polluted Countries:
A Control Variable Analysis
6. Towards a Green Economy: The Role of Green Financing in Reducing Air Pollution in
Top Polluted Countries
Research Model:
Green
Financing
(GF)
Gross
Domestic
Product
(GDP)
Adoption of
Renewable Energy
Sources (RES)
Level of Economic
Development (ED)
Air Quality
Index (AQI)
Government
Commitment to
Reducing Air
Pollution (GC)
Econometric Model:
AQI = β0 + β1GF + β2RES + β3ED + β4GC + β5GDP + ε
Where:
AQI = Air Quality Index
GF
= Green Financing
RES = Adoption of Renewable Energy Sources
ED
= Level of Economic Development
GC
= Government Commitment to Reducing Air Pollution
GDP = Gross Domestic Product
β0, β1, β2, β3, β4, β5 = Regression coefficients
ε
= Error term
There are Logical Relationship in these variables:
1. Independent Variable: Green Financing (GF)
Green Finance refers to the financial support given to new projects and initiatives that promote
environmental sustainability, particularly those related to reducing greenhouse gas emissions and
improving air quality. The GF variable is the main focus of the study, which an increase in Green
Financing will lead to improve air quality of the country.
2. Dependent Variable: Air Quality Index (AQI)
The Air Quality Index variable represents the level of air pollution in a given country. The study
aims to investigate the impact of Green Financing on air quality, as measured by the Air Quality
Index.
3. Adoption of Renewable Energy Sources (RES) Mediating Variable
The Adoption of Renewable Energy Sources variable represents the scope. which countries are
using renewable energy sources as a replacement for fossil fuels. However Green Financing will
have an impact on air quality indirectly, through the adoption of renewable energy sources. As
Green Financing increases, it is expected that more renewable energy sources will be adopted,
which in try will lead to improved air quality.
4. Level of Economic Development (ED) Moderating Variable
The Level of Economic Development variable refers to the level of development of a country's
economy. The relationship between Green Financing and air quality will be moderated by the level
of economic development. This means that the impact of Green Financing on air quality will be
depending on the level of economic development. It is expected that Green Financing will have a
stronger impact on air quality in less developed countries.
5. Government Commitment to Reducing Air Pollution (GC) Instrumental Variable
The Government Commitment to Reducing Air Pollution variable refers to the level to which
governments are committed to reducing air pollution. This variable is expected to strengthen the
relationship between Green Financing and air quality. If governments are more committed to
reducing air pollution, it is expected that Green Financing will have a greater impact on air quality.
6. Gross Domestic Product (GDP) Control Variable (I Need more understanding
regarding this variable)
The Gross Domestic Product variable represents the total economic output of a country. The
relationship between Green Financing and air quality. By including GDP as a control variable, the
study aims to isolate the effect of Green Financing on air quality.
How to measure these variables?
Green bonds are used as a proxy for green finance.
There are following research papers which have published in top ranking journals
Journal Name
Impact Factor
Title of Paper
Volume
Details
International Review of Financial Analysis
8.235
Green finance and investment behavior of renewable energy
enterprises: A case study of China
87 Latest Issue of 2023
Variables:
1.
2.
3.
4.
Green finance
Investment of Renewable Energy Enterprises
Economic Development (GDP)
Financing Constraints (FC)
Conceptual Definitions:
1. Green Finance development index
This variable used in this study as Explanatory. The specific variables for constructing the
green finance development index are defined in the below table.
Variables
Green Credit
Green Securities
Green Investment
Green Insurance
Definition
The proportion of the interest expense of energy consuming and
polluting industries as a percentage of the interest expense of all
industries above a designated size.
The proportion of the total market value of environmental
protection enterprises as a percentage of the total market value of
A-shares. The proportion of the total market value of energyintensive enterprises as a percentage of the total market value of
A-shares.
The proportion of fiscal support for energy conservation and
environmental protection as a percentage of total regional fiscal
expenditure. The proportion of investment in environmental
protection and pollution control as a percentage of total regional
GDP. The proportion of agricultural insurance expenditure as a
percentage of total insurance expenditure.
The proportion of agricultural insurance expenditure as a
percentage of agricultural insurance income.
Green Bonds
The proportion of green bonds as a percentage of various
domestic bonds.
2. Investment of Renewable Energy Enterprises
This variable used in this study as explained variable. There are two definitions of investment of
renewable energy enterprises.


The first one is from an investee perspective, investment of renewable energy enterprises
refers to the funds that enterprises invest in the field of renewable energy.
The second is from an investor perspective, investment of renewable energy enterprises is
the investment that renewable enterprises use to purchase fixed assets, intangible assets,
and other long-term assets (Zhang, Cao, & Zou, 2016).
3. Economic Development (GDP)
The variable of Economic Development used in this paper as Threshold. Economic development
refers to the scale, speed, and level achieved by a region’s economic development. Economic
Development is programs, policies or activities that seek to improve the economic well-being and
quality of life for a community.
What “economic development” means to you will depend on the community you live in. Each
community has its own opportunities, challenges, and priorities. Your economic development
planning must include the people who live and work in the community.
4. Financing Constraints (FC)
Financing constraints refer to the difficulties faced by firms in obtaining external financing for
investment projects due to limited access to credit markets or high costs of borrowing. These
constraints can hinder the growth and development of firms, particularly small and medium-sized
enterprises (SMEs), and limit their ability to undertake profitable investment projects.
According to the World Bank, financing constraints arise when firms face significant obstacles to
accessing external finance, such as lack of collateral, high interest rates, or limited availability of
credit. These constraints can result in a reduction in investment, lower productivity, and slower
economic growth.
Operational Definitions/Data Measurements.
1. Green Finance development index
The data related to the green finance come from the China Industrial Statistical Yearbook, Finance
Yearbook of China, China Environmental Statistical Yearbook and the official website of the
People’s Bank of China.
2. Investment of Renewable Energy Enterprises
Renewable energy enterprises in China’s A share market as the study sample, with data from 2007
to 2019. According to the 2017 edition of “The industrial Classification Guidelines” issued by the
China Securities Regulatory Commission, renewable energy enterprises include those associated
with the “Electricity, Heat, Gas and Water Production and Supply Industry (Industry code are D44,
D45, and D46)” and “Ecological Protection and Environmental Management Industry (Industry
code is N77).” The data related to the renewable energy enterprises are from the Wind database;
data on the level of economic development are from the National Bureau of Statistics of China
3. Economic Development (GDP)
The level of economic development refers to the scale, speed, and level achieved by a region’s
economic development. Instead of adopting total value of economic growth, this paper analyzes
the GDP growth rate to measure the economic development level. This approach has the advantage
of more accurately reflecting the changes in economic development from an incremental
perspective.
4. Financing Constraints (FC)
The financing constraints faced by enterprises are closely related to their investment behavior.
Based on Musso and Schiavo (2008), 4 this paper constructs a synthetic financing constraint index
from five indicators: firm size, interest coverage ratio, net operating capital, asset-liability ratio,
and ability to generate cash flow. The financing constraint index ranges from 1 to 5. A higher value
is associated with the enterprise’s ability to bear greater financing pressure.
Journal Name
Impact Factor
Title of Paper
Volume
Model:
Details
Environmental Science and Pollution Research
5.19
Green finance and corporate environmental violations: a test
from the perspective of illegal pollution discharge behaviors
July 2022 issue 32
Details
Journal Name
Impact Factor
Title of Paper
Volume
Borsa Istanbul Review
4.288
How does green finance asymmetrically affect greenhouse gas emissions?
Evidence from the top-ten green bond issuer countries
March 2023
The present study analyzes the asymmetric association between green finance and greenhouse gas
emissions in the top ten countries that support green finance (China, Canada, France, Germany,
Japan, the Netherlands, Spain, Sweden, the UK, and the US). Previous research employed panel
data methods, resulting in consistent outcomes concerning the association between green finance
and environmental quality, regardless of the fact that many countries did not generate such a
relationship individually. The present study, however, uses the quantile-onquantile technique,
which enables us to assess time-series dependence in each country independently. We find that
green finance enhances environmental quality by curtailing greenhouse gas emissions in most of
the economies studied at specific quantiles of the data. Moreover, the level of asymmetry among
our variables changes by country, focusing on the need for policy makers to pay particular attention
in implementing green finance and environmental sustainability policies.
Variables:
1. Green Finance
2. Greenhouse Gas Emission
The dataset for our investigation comprises two variables. GF is our independent variable that is
proxied by Green Bond (GB)
Details
Journal Name
Impact Factor
Title of Paper
Volume
Geoscience Frontiers
7.483
Does nuclear energy reduce carbon emissions despite using fuels and
chemicals? Transition to clean energy and finance for green solutions
April 2023
Green power conversion is the shift away from traditional fuels towards clean energy sources such
as nuclear power plants, hydroelectric dams, wind farms, and solar panels. This research examines
the impact of clean energy demand and green financing on reducing carbon emissions in 29
economies in Europe and Asia from 2007 to 2020. The study used a two-step differenced GMM
estimator for the available data set spanning 2007 to 2020. The study found that rising demand for
nuclear power helps to achieve a carbon-neutral agenda, but insufficient funding for renewable
energy leads to higher carbon emissions. The research suggests increasing investment in nuclear
energy and green financing can improve regional environmental quality. The study found a causal
link between fuel imports, nuclear power and regional growth. It also determined that fuel imports,
chemical use, green financing and the need for nuclear energy will likely impact regional
environmental quality. The research recommends allocating more resources toward innovation to
boost energy efficiency and expanding investment in renewable and nuclear energy production
industries via green finance. The study also highlights the need to encourage the development of
renewable energy sources to cut carbon emissions and establish a sustainable society.
Variables:
1. clean energy demand
2. green financing
3. The dependent variable is CO2 emissions (metric tons per capita)
The independent variables are alternative and nuclear energy (% of total energy use), renewable
energy consumption (% of total final energy consumption), green field investment is taken as a
proxy of gross capital formation (% of GDP), fuel imports (% of merchandise imports), and
chemical use (% of value added in manufacturing)
I.
II.
III.
IV.
Carbon emissions and chemical use driven by imports: The continued reliance on nonrenewable fuel imports is a barrier to implementing a green developmental strategy.
The causation inferences supported the direct link between fuel imports, carbon
emissions, and chemical usage. As a result, it is crucial to reduce fuel imports and seek
out renewable energy sources for use in value-added manufacturing, reducing chemical
consumption.
Carbon-driven green financing and chemical utilization: Carbon emissions Granger
cause greenfield investment and chemical use, leading to worst economic and health
outcomes. The panic caused by the environment’s deterioration prompted politicians
to adopt green finance schemes and cleaner industrial methods, propelling economies
toward continuous growth.
Greenfield investment boosts more environmentally friendly energy supply: This aids
economies in reducing energy shortages and reaching a zero-carbon agenda, and
Connections between Greenfield Investing, Nuclear Power Demand, and Fuel Imports:
Alternative nuclear energy consumption, sustainable finance, and fuel imports are all
connected via a feedback loop. It highlights the need for caution while creating
sustainable regional strategies to restrict fuel imports via sustainable finance and
nuclear power.
Details
Journal Name
Impact Factor
Title of Paper
Volume
Borsa Istanbul Review
4.288
Green finance and green transition by enterprises: An exploration
of market-oriented governance mechanisms
Jan 2023
Using the implementation of the “Guidelines for Establishing the Green Financial System”
(GEGFS) released in China in 2016 as a natural experiment, we adopt the difference-in-differences
(DID) method to explore the influence of a green finance (GF) policy on the green transition of
enterprises (GTE) from the perspective of its market-oriented governance mechanism. We obtain
three findings from our empirical results. First, GF can significantly drive GTE through providing
market-oriented governance. Second, the market-oriented governance by GF drives GTE via two
channels: green oversight and green governance. Third, the promotional effect of GF is greater in
areas with strong environmental governance by the state, at firms with less public environmental
oversight, and at firms that actively disclose green information. Our results not only enrich the
relevant literature on GF and GTE but shed light on how and the extent to which GF can help to
achieve a green transition in the economy through the use of market-oriented governance.
Variables:
I.
II.
Green Transition of Enterprises (GTE)
Green Finance
Details
Journal Name
Impact Factor
Title of Paper
Volume
Ecological Economics
6.536
The impact of fintech innovation on green growth in China: Mediating
effect of green finance
March vol.193, 2022
Although green growth has become the economic development strategy of many countries in the
world, and studies have analyzed the influencing factors of green growth from multiple angles,
there are few literatures devoted to the impact of fintech and green finance on green growth. From
the perspective of fintech development, this paper tries to construct a comprehensive index to
evaluate the green growth of regional economy based on the in-depth analysis of the influence
mechanism of green finance on green growth. At the same time, China’s provincial panel data
from 2011 to 2018 are selected to test the impact of fintech innovation and green finance on green
growth, and its mechanism. It turns out that fintech and green finance significantly promotes green
economic growth. At the same time, the impact of fintech and green finance on green growth has
obvious regional heterogeneity, that is, the impact in eastern China is significantly stronger than
that in central and western China. Further research shows that fintech innovation mainly promotes
green economic growth through green credit and green investment. Therefore, fintech innovation
can promote green economic growth by improving the development level of green finance, which
has great reference significance for most countries.
Model
Variables:
I.
II.
III.
Explained variable: Green growth
Explanatory variable: fintech innovation level
The control variables are: Capital it is the proportion of scientific research and technical
personnel in the total population, Labor.