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Author: Zhao Jing, Associate Professor, School of Economics and Management, Xi'an University of Technology, Master Instructor
Mailbox: [email protected]
Today, our circle and the Environmental Energy Research Group jointly introduced an article from our community friends, which involves the use of spatial measurement and interactive items related methods. Scholars can read the following materials first, and then compare the published articles for in-depth understanding.
Here are some articles related to spatial econometrics, which scholars can refer to:
1. The latest developments and theoretical framework of spatial econometrics
2. Measurement of space and time, focusing on two Chinese
3. Spatial measurement model selection, estimation, weighting, and testing
4. The do file of the space measurement encyclopedia-style usage guide is released
5. An encyclopedia-like user guide for space metrology, this is the only one to master this unique cheat
6. 46 pages of Notes on space measurement, available for reference by scholars
7. Collection of spatial measurement software code resources (Matlab/R/Python/SAS/Stata)
8. Use R language to do space measurement, a concise tutorial that must not be missed
9. Overview of Spatial Econometrics Packages in R Software
10. Research domain model, development stage and latest progress of space metrology
The following are some articles related to interaction items, which scholars can refer to:
1. Case study of the use and interpretation strategy guide of cross items in empirical research
1. What is the difference between interaction items and group regression? Heterogeneity analysis
2. Econometric regression What the hell is the interaction term? I bring you a book
3. 5 questions and responses related to the "interaction term" in econometrics
4. What happens to the decentralized interaction term in panel data?
5. Interaction of endogenous variables How to find the instrumental variables for the items, and what to do if the interaction items are collinear
. 6. The fixed effect of the intersection of the province/industry fixed effect and the year fixed effect
7. The U-shaped and inverted U-shaped relationship and its moderating effect, and the nonlinear relationship is advanced a little
8 .Self-service test of mediation and moderating effects, aiming at non-normal cross-section data
9. Control, moderation, and mediation variables, based on
10. Mediation effects procedures and data
with moderating variables , exclusive interpretation of relevant results 11. Mediation effects with moderating variables Analysis, moderated mediation
12. Intermediary and moderation effect operation guide, classic books and PPT collector's edition
The three authors used panel data from 30 provinces in China from 1999 to 2017 to examine the impact of China's economic growth, financial development, and energy consumption on environmental pollution through the spatial Dubin model. In order to ensure the robustness of the estimation, the paper uses two indicators of financial development depth and financial development efficiency to characterize financial development. The study found that financial development has direct and indirect effects on environmental pollution. The depth of financial development and the efficiency of financial development have negative and positive direct effects on environmental pollution, respectively. In addition, the depth of financial development and the efficiency of financial development both negatively regulate the impact of technological progress on environmental pollution. At the same time, the depth of financial development positively regulates the impact of industrial structure on environmental pollution. The study also found that there is an "inverted-N" environmental Kuznets curve relationship between economic growth and industrial sulfur dioxide and industrial solid waste, but there is no significant environmental Kuznets curve relationship between industrial wastewater and economic growth.
1. Introduction:
Since the reform and opening up in 1978, China has experienced rapid economic growth and rapid financial development, especially since the large-scale reform of the financial system in the new century has led to the vigorous development of the financial industry. But with this, the total amount of pollutants (such as sulfur dioxide) emitted by China ranks among the top in the world (World Bank, 2007), and China has become one of the world's largest energy consumers (Li et al., 2016). Due to the limitation of resource endowments, 70% of China's energy consumption comes from coal (Liu et al., 2018). The burning of fossil fuels such as coal and petroleum produces a large number of pollutants, including sulfur dioxide (SO2) and solid waste (Bi et al., 2014). The discharge of these pollutants seriously affects residents' health and ecosystems, and may cause lung dysfunction, respiratory diseases, and acid rain (Venners et al., 2003; Wei et al., 2014; Song et al., 2016).
In view of the huge damage caused by these pollutants, the Chinese government began to strengthen the management and control of environmental pollutants in the 1990s. Through continuous and effective efforts over the years, China’s sulfur dioxide emissions have dropped from a peak of 25.88 million tons in 2006 (the world’s largest emitter) to 8.75 million tons in 2017 (the world’s second largest emitter). China's industrial solid waste production has also been declining after reaching a peak of 3,325.09 million tons in 2012.
With the rapid development of China's economy and finance, China's environmental pollutant emissions have fallen sharply after entering the new century. Therefore, examining how China's financial development affects environmental pollution is of great significance to improving the environmental performance of China and the world. If financial development can curb pollutant emissions, then a large number of developing countries in the world can learn from China's development model. Recent studies have focused on this issue in the context of China, but these studies have ignored the complex mechanisms of financial development's impact on the environment (Zhang, 2011; Yuxiang and Chen, 2011).
Driven by the financial system, capital flows from inefficient companies to more efficient companies, which can eliminate backward production capacity (Zhang, 2011) and reduce pollution output. In addition, financial intermediaries can increase the speed of technological innovation (King and Levine, 1993; Ilyina and Samaniego, 2011) and stimulate technological progress (Frankel and Romer, 1999). Birdsall and Wheeler (1993), Frankel and Rose (2002), and Jalil and Feridun (2011) found that developing countries can obtain new environmental protection technologies through financial development, and this technological advancement can significantly reduce pollutant emissions. On the other hand, many scholars pointed out that financial development will also stimulate heavy industry to expand investment, install new equipment and increase production capacity, which may increase pollutant emissions (Dasgupta et al., 2001; Tamazian and Rao, 2010; Sadorsky, 2010; Zhang, 2011; Shahbaz et al., 2012, 2013).
Existing relevant empirical studies have found that financial development has positive, negative and no impact on the environment. The possible reason for such controversial results is that the mechanism of financial development affecting the environment is complicated. The above studies all use financial development indicators directly as explanatory variables to estimate the direct impact of financial development on environmental performance. However, financial development may also have an indirect impact on environmental pollution through factors such as technology and industrial structure (Hao et al., 2016b).
In addition, most empirical studies in China use carbon dioxide emissions as an indicator of environmental pollution, but this indicator is difficult to measure the overall characteristics of environmental pollution. Compared with carbon dioxide, industrial wastewater, sulfur dioxide, and solid waste (hereinafter referred to as the industrial "three wastes") have more significant direct and indirect impacts on China's public health and environmental safety (Zhang et al., 2014; Xia et al. , 2017). Therefore, the paper chooses these three types of pollutant emissions as indicators of environmental pollution, so that it can cover gas, liquid and solid pollutants.
Innovation:
1. A quantitative analysis of the mechanism of financial development affecting environmental pollution in various provinces in China. In this process, we emphasized the important moderating role of financial development between environmental pollution and two variables (ie, industrial structure and technological progress).
2. Use appropriate spatial measurement methods to control spatial correlation. Ignoring spatial correlation may lead to incorrect inferences and poor model performance (Maddison, 2006; Wang et al., 2013; Hao et al., 2016a; Wang and He, 2019).
2. Model selection:
Referring to the ideas of Tamazian et al. (2009) and Jalil and Feridunr (2011), we put economic growth, financial development and environmental pollution in a multivariable environmental Kuznets curve framework.
The following is the model design:
Use interactive items to carry out mechanism analysis:
For model selection, we use the most commonly used spatial measurement models: Spatial Lag Model (SLM), Spatial Error Model (SEM) and Spatial Durbin Model (SDM).
Spatial measurement model design:
3. Data and variables
The paper uses provincial-level regional panel data from 30 provinces in mainland China from 1999 to 2017. Due to the lack of data on energy consumption and environmental pollution, the Tibet Autonomous Region did not include the study sample. In addition, based on the availability and completeness of the data, Hong Kong, Macau Special Administrative Region and Taiwan are not included.
Variables used and definitions:
4. Empirical results and conclusions
Using panel data from 30 provinces in China from 1999 to 2017, the paper uses the Spatial Durbin Model (SDM) to control spatial dependencies, eliminate estimation bias, and study the impact of economic growth, financial development, and energy consumption on China’s environmental pollution. The paper also did some robustness tests (using different methods and alternative indicators), and found that the main results are robust.
Moran Index:
The main conclusions are as follows:
(1) The emissions of the three types of pollutants in 30 provinces in my country all have significant spatial agglomeration effects, and the spatial correlation of environmental pollution in various provinces is gradually increasing.
(2) There is an "inverted N" type EKC relationship between my country's SO2 emissions and GDP per capita, solid waste emissions and GDP per capita. In addition, the provinces of China have not yet reached the second turning point of the "inverted-N" EKC. This turning point has occurred after fully considering the spatial dependence. At the same time, there is no evidence that there is an EKC relationship between wastewater discharge and GDP per capita.
(3) Energy consumption has increased environmental pollution, and the two indicators of financial development have different impacts on environmental pollution. The development of financial depth has generally reduced the emission of SO2 and solid waste, while the improvement of financial efficiency has generally led to an increase in the emission of SO2 and solid waste. In addition, the development of financial depth and financial efficiency has a negative regulatory effect on the impact of technological progress on SO2 and solid waste emissions. At the same time, the development of financial depth has a positive regulatory effect on the impact of the development of secondary industries on SO2 and solid waste emissions. In addition, financial development has affected China's wastewater discharge.
The regression results for SO2, while solid waste and wastewater discharge are similar:
Regarding the direct and indirect effects of economic growth, energy consumption and financial development on environmental pollution:
Long press the above two-dimensional code to view and download the full text of the
first author's profile: Zhao Jing, from Jingzhou City, Hubei Province, Ph.D., associate professor of the School of Economics and Management, Xi'an University of Technology, master's tutor. Presided over 1 National Natural Science Foundation of China, 1 National Social Science Foundation, and 9 provincial and ministerial projects. Won 2 Shaanxi Science and Technology Awards and 3 Shaanxi Philosophy and Social Science Outstanding Achievement Awards. Zhao Jing was rated as the "Young Science and Technology Star of Shaanxi Province" in 2013, and was selected as the "Young Talent Support Program" of Shaanxi Universities in 2018.
Extended reading:
1. The types of double differential DID are broken down, 20 articles that have to be read
2. The endogenous problem operation guide, 22 widely circulated articles
3. The matching method (matching) operation guide, 16 articles worth collecting
4. An encyclopedia-style guide for spatial metrology, this is the only one to master this unique cheat.
5. Panel data model operation guide, 16 articles you have to read
6. Social network analysis latest literature and software learning manual
7. Chinese industrial enterprise database Use problem description
8. The latest research trends and hot topics of JEEM environmental and resource economics
9. Statistics of the review status of English journals in the field of economics, number of review cycles
10. Full statistics of review information of Chinese C journals, number of review cycles , Download as soon as possible
11. Use Stata's 115-page Tips efficiently, the PDF version can be printed and used
1. Anshen + Clark Prize winner's RDD paper, breakpoint regression design
2. What is the breakpoint regression design in policy evaluation, and how to do it?
Volume 3.2 RDD Breakpoint Regression User Manual, including Stata and R software operating procedures
4. Breakpoint regression design RDD is fully explained, and there are many users in the education field
5. Without instrumental variables, breakpoints and random shocks, attribution can also be inferred
6. RDD breakpoint regression, an encyclopedia of empirical complete programs
7. Breakpoint regression design RDD classification and operation case
8. Comparison of four methods of DID, synthesis control, matching, RDD, applicable scope and characteristics
9. RDD classic literature, RDD model validity and robustness test
10.Do you use IV, RDD, DID, PSM more in the education field?
11. RDD slides data public sharing
12. Frontier research status of breakpoint regression design, RDD
13.PSM-DID, DID, RDD, Stata program encyclopedia
14. What should I do if I can’t find IV, RD and DID? This is an alternative method.
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6. Is Yao Yang economics a science? What the editor-in-chief of the quarterly said
7. Yao Yang, the editor-in-chief of the Economic Quarterly: How to write a high-quality paper
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15. Wu Xizhi 's 7 major misunderstandings in econometrics, have you stepped on the thunder?
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