Discussion Paper

No. 2019-13 | February 12, 2019
Can reducing carbon emissions improve economic performance? Evidence from China

Abstract

As the problem of carbon emissions is becoming increasingly more serious around the world, how to balance carbon emissions reduction and economic growth has become an important issue in the field of ecological economics. China is the world's largest carbon dioxide emitter, and China's Low-Carbon Pilot (CLCP) policy has significantly reduced carbon dioxide emissions and achieved expected benefits. However, is environmental quality improving at the expense of economic growth?   Based  on   panel  data  from  286   Chinese   prefecture-level   cities  and  from  Chinese micro-industrial  enterprises  from  2001  to  2013,  this  article  focuses  on  the  causal  effect  of environmental policy on regional economic growth and the benefits and changes in the behavior of enterprises through a quasi-natural experiment and the difference-in-differences (DID) method. The results are as follows. First, the CLCP policy significantly promotes regional economic growth. Moreover, as the implementation time of the policy continues, environmental regulation has a greater effect of promoting economic growth. Second, although the CLCP policy significantly increases various production costs, it also promotes the growth of enterprises' output and benefits. Third, under the pressure of the significant increase in enterprise cost caused by environmental regulation, enterprises choose the positive way of strengthening internal management, improving efficiency and increasing innovation instead of choosing the negative way of trans-regional transfer to exit the market; accordingly, enterprises finally achieve an improvement in output and benefits.

Data Set

JEL Classification:

O12, O13, Q38

Assessment

  • Downloads: 207

Links

Cite As

Fei Yang, Beibei Shi, Ming Xu, and Chen Feng (2019). Can reducing carbon emissions improve economic performance? Evidence from China. Economics Discussion Papers, No 2019-13, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2019-13


Comments and Questions


Luca Riccetti - Comments to Yang et al. (2019)
March 21, 2019 - 13:12

The paper of Yang et al. (2019) is a very interesting study. It performs an econometric analysis based on a “quasi-natural experiment”: in July 2010, China started the China’s Low-Carbon Pilot policy (CLCP), that is, five provinces and eight cities were selected to be the pilot areas for controlling greenhouse ...[more]

... gas emissions. The authors study the effects of the environmental regulation both at macroeconomic and microeconomic levels.
At macroeconomic level, they find that it significantly improves regional GDP and per capita GDP and this effect grows year after year. Moreover, environmental regulation is conducive to the export competitiveness of the clean industry and improves the accumulation of regional human capital. A series of robustness checks confirm these results.
At microeconomic level, they find that firms strengthen management, improve efficiency and increase investment in innovation activities to gain greater competitiveness, and all these activities offset (and not only offset) the increase in production costs caused by environmental regulation. In addition, enterprises could choose to transfer across regions to avoid the impact of environmental regulation, but they decide improving operating efficiency and carrying out innovation instead of supporting transfer costs.
These results are extremely relevant because a substantial reduction of greenhouse gas emissions is immediately needed. However, many economists, entrepreneurs and politicians often stop or slow down this process on the basis of studies on the relationship between environmental regulation and economic growth that do not arrive to a unified conclusion. The success of this "quasi-natural experiment" is surely a very relevant result with extremely important policy implications.
Some remarks (beside some typos):
- I am not very convinced by the use of the logarithm of the GDP, that is surely a not stationary variable. I suggest to perform the analysis on GDP growth rate.
- Size, debt, right and also labor are very connected variables and they could present multicollinearity problems. I suggest to check the Variance Inflation Factors.
- Figure 2 and 3 seems to show a starting trend already in 2009. I suggest to check this feature that could partially weaken the results.
- The “PSM-DID Method Test” section seems to be added later. Please explain better what PSM is.


Beibei SHI - reply
March 29, 2019 - 10:40

(1)I am not very convinced by the use of the logarithm of the GDP, that is surely a not stationary variable. I suggest to perform the analysis on GDP growth rate.

First, thank you very much for your attention to variable selection in this article. In order to ...[more]

... further illustrate the robustness of the results, the rate of GDP growth(g) is selected here to measure economic growth. The regression results are shown in columns (1) and (2) in TABLE 1. It can be seen that the regression results do not change significantly, indicating that the difference in index selection does not affect the conclusions of this study.


(2)Size, debt, right and also labor are very connected variables and they could present multicollinearity problems. I suggest to check the Variance Inflation Factors.

By performing a multicollinearity test on the above variables, it is known that Mean VIF=4.05, which is much smaller than 10, indicating that there is no serious multicollinearity between the variables.



(3)Figure 2 and 3 seems to show a starting trend already in 2009. I suggest to check this feature that could partially weaken the results.

Thank you for your suggestion of this detail. The reason why such a result appeared in 2009 is that policy making may lead companies to be aware of it in advance and affect its behavior in advance. To ensure the accuracy of the assessment results, we excluded this potential impact by eliminating the 2009 sample. The results are shown in columns (3)-(6) in TABLE 1. It can be seen that the results do not changed significantly.


(4) The “PSM-DID Method Test” section seems to be added later. Please explain better what PSM is.


Propensity Score Matching (PSM) is a statistical method used to process data of Observational Study. In observational studies, for various reasons, there are a lot of data biases and confounding variables. PSM can reduce the effects of data biases and confounding variables for a more reasonable comparison between the experimental and control groups. It was first proposed by Paul Rosenbaum and Donald Rubin in 1983 and is commonly used in medicine, public health, economics and so on.