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Does environmental regulation affect CO2 emissions? Analysis based on threshold effect model

  • Yanan Wang
  • Yihui Zuo
  • Wei Li
  • Yanqing Kang
  • Wei Chen
  • Minjuan Zhao
  • Haibin Chen
Original Paper
  • 60 Downloads

Abstract

With the increasing pressure on China to reduce carbon dioxide (CO2) emissions, it is crucial to clarify the effect of implementing environmental regulations and their impact on the region. Many studies have focused on the linear, rather than nonlinear, relationship between environmental regulation and CO2 emissions. The exploration of nonlinear relations is conducive to the in-depth study of policy effects and regional differences of environmental regulations in China. To ensure effective CO2 emission reductions, regional differences in CO2 emissions in China should also be considered. In this study 30 provinces of China were divided into three different regions according to their level of economic development from 2004 to 2015. Taking the energy intensity and foreign direct investment (FDI) as threshold variables, a threshold model was used to examine the relationship between environmental regulation and CO2 emissions. It was found that environmental regulation has a threshold effect on CO2 emissions, with significant differences among the eastern, central, and western regions. Environmental regulations in the eastern region were ineffective for curbing CO2 emissions, while the energy intensity was in the middle and low threshold range. However, FDI had a promotional effect on CO2 emissions. In the central region, environmental regulations reduced CO2 emissions under the influence of energy intensity and FDI. In the western region, environmental regulations could not mitigate CO2 emissions when the energy intensity and FDI were used as the threshold variables. It was concluded that a diverse range of measures for CO2 reduction should be adopted according to the local economic situation.

Graphical abstract

Keywords

CO2 emissions Environmental regulation Threshold mode Regional difference 

Abbreviations

FDI

Foreign direct investment

CO2

Carbon dioxide emissions

P

Population

A

GDP per capita

URB

Urbanization

ER

Environmental regulation

Indices

X

Explanatory variable

Y

Dependent variable

th

The threshold variable

γ

The assumed threshold value

ε

An independent scalar

μ

The fixed effect

I(·)

An indicator function of 0 or 1

β

The slope coefficients

ρ

The slope coefficients

α

The coefficient matrix

M

The variable matrix

αT

The transposed coefficient matrix

Y*

The matrix form of the function

e*

The residual for the regression function

SSE

The sum of squared errors

\(\hat{\lambda }\)

The threshold value

\(H_{0}^{1}\)

The null hypothesis

\(H_{1}^{1}\)

The alternative hypothesis

F1

The value of F-statistic

S0

The sums of squared residuals under \(H_{0}^{1}\)

S1

The sums of squared residuals (SSR) under\(H_{1}^{1}\)

\(\hat{\gamma }\)

The OLS estimate of γ

\(\hat{\sigma }^{2}\)

The residual variance under\(H_{1}^{1}\)

LR1

The value of LR statistic

i

One of the provinces

t

One of the years

Notes

Acknowledgements

This study was funded by Project of Humanities and Social Sciences of the Ministry of Education of China (18XJC790014); the National Natural Science Foundation of China (71803152, 71503200); the Natural Science Basic Research Program of Shaanxi Province (No. 2018JQ7006); and the Fundamental Research Funds for the Central Universities (2017RYWB01, 2017RWYB06); the authors would like to thank the anonymous referees for their helpful suggestions and corrections on the earlier draft of our paper.

References

  1. Barido DP, Maishall JD (2014) Relationship between urbanization and CO2 emissions depends on income level and policy. Environ SciTechnol 48(7):3632–3639CrossRefGoogle Scholar
  2. Bella G, Massidda C, Mattana P (2014) The relationship among CO2, emissions, electricity power consumption and GDP in OECD countries. J Policy Model 36(6):970–985CrossRefGoogle Scholar
  3. Cao Z, Wei J, Chen HB (2016) CO2, emissions and urbanization correlation in China based on threshold analysis. Ecol Indic 61:193–201CrossRefGoogle Scholar
  4. Chaabouni S, Saidi K (2017) The dynamic links between carbon dioxide (CO2) emissions, health spending and GDP growth: a case study for 51 countries. Environ Res 158:137–144CrossRefGoogle Scholar
  5. Chan KS (1993) Consistency and limiting distribution of the least squares estimator of a threshold autoregressive model. Ann Stat 21:520–533CrossRefGoogle Scholar
  6. Cheikh NB, Louhichi W (2016) Revisiting the role of inflation environment in exchange rate pass-through: a panel threshold approach. Econ Model 52:233–238CrossRefGoogle Scholar
  7. Chen W, Shen Y, Wang Y, Wu Q (2018) How do industrial land price variations affect industrial diffusion? Evidence from a spatial analysis of China. Land Use Policy 71:384–394CrossRefGoogle Scholar
  8. Chikaraishi M, Fujiwara A, Kaneko S, Poumanyvong P, Komatsu S, Kalugin A (2015) The moderating effects of urbanization on carbon dioxide emissions: a latent class modeling approach. Technol Forecast Soc Change 90:302–317CrossRefGoogle Scholar
  9. Cole MA, Elliott RJR, Shimamoto K (2005) Industrial characteristics, environmental regulations and air pollution: an analysis of the UK manufacturing sector. J Environ Econ Manag 50(1):121–143CrossRefGoogle Scholar
  10. Crew MA, Heyes A (2013) Market-based approaches to environmental regulation: Editors’ introduction. J Regul Econ 44(1):1–3CrossRefGoogle Scholar
  11. Daron A, Philippe A, Leonardo B, David H (2012) The environment and directed technical change. Am Econ Rev 102(1):131–166CrossRefGoogle Scholar
  12. Dasgupta S, Laplante B, Wang H, Wheeler D (2002) Confronting the environmental Kuznets curve. J Econ Perspect 16(1):147–168CrossRefGoogle Scholar
  13. Eichner T, Pethig R (2011) Carbon leakage, the green paradox and perfect future markets. Int Econ Rev 52(3):767–805CrossRefGoogle Scholar
  14. Gonzalez A, Terasvirta T, Dijk DV (2005) Panel smooth transition regression models. SEE/EFI Working Paper Series in Economics and Finance, No. 604Google Scholar
  15. Grafton RQ, Kompas T, Long NV (2012) Substitution between biofuels and fossil fuels: is there a green paradox? J Environ Econ Manag 64(3):328–341CrossRefGoogle Scholar
  16. Guo F, Zhao T, Wang Y, Wang Y (2016) Estimating the abatement potential of provincial carbon intensity based on the environmental learning curve model in China. Nat Hazards 84(1):685–705CrossRefGoogle Scholar
  17. Hansen BE (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. J Econ 93(2):345–368CrossRefGoogle Scholar
  18. IEA (2017) CO2 emissions from fuel combustion. Organization for Economic Co-operation and Development (OECD) Press, ParisGoogle Scholar
  19. Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115(1):53–74CrossRefGoogle Scholar
  20. IPCC (2014) Climate change (2014) Synthesis report. Cambridge University Press, CambridgeGoogle Scholar
  21. IPCC (2007) Climate change (2007) mitigation of climate change. Working Group III Contribution to the Fourth Assessment Report of the IPCC. Cambridge University Press, CambridgeGoogle Scholar
  22. Jaffe AB, Stavins RN (2004) Dynamic incentives of environmental regulations: the effects of alternative policy instruments on technology diffusion. J Environ Econ Manag 29(3):S43–S63CrossRefGoogle Scholar
  23. Jaffe AB, Peterson SR, Portney PR, Stavins RN (1995) Environmental regulation and the competitiveness of U.S. manufacturing: What does the evidence tell us? J Econ Lit 33(1):132–163Google Scholar
  24. Kang YQ, Xie BC, Wang J, Wang YN (2018) Environmental assessment and investment strategy for China’s manufacturing industry: a non-radial DEA based analysis. J Clean Prod 175:501–511CrossRefGoogle Scholar
  25. Leonard HJ (1988) Pollution and the struggle for the world product. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  26. Li W, Zhao T, Wang Y, Guo F (2017) Investigating the learning effects of technological advancement on CO2 emissions: a regional analysis in China. Nat Hazards 88(9):1–17Google Scholar
  27. Maddala GS, Wu S (1999) A comparative study of unit root tests with panel data V and a new simple test. Oxf Bull Econ Stat 61(S1):631–652CrossRefGoogle Scholar
  28. Marjanović V, Milovančević M, Mladenović I (2016) Prediction of GDP growth rate based on carbon dioxide (CO2) emissions. J CO2 Util 16:212–217CrossRefGoogle Scholar
  29. Martínez-Zarzoso I, Maruotti A (2011) The impact of urbanization on CO2 emissions: evidence from developing countries. Ecol Econ 70:1344–1353CrossRefGoogle Scholar
  30. Mitchell B (1992) Getting it green: case studies in Canadian environmental regulation. J Agric Environ Ethics 5(2):235–239CrossRefGoogle Scholar
  31. Nadeau LW (2004) EPA effectiveness at reducing the duration of plant-level noncompliance. J Environ Econ Manag 34(1):54–78CrossRefGoogle Scholar
  32. Nässén J, Larsson J (2015) Would shorter working time reduce greenhouse gas emissions? An analysis of time use and consumption in Swedish households. Environ Plan C 33(4):726–745CrossRefGoogle Scholar
  33. Oates WE, Palmer K, Portney PR (1993) Environmental regulation and international competitiveness: thinking about the Porter hypothesis. Resour Future Work Pap No. 94-02Google Scholar
  34. Ozturk I, Aslan A, Kalyoncu H (2010) Energy consumption and economic growth relationship: evidence from panel data for low and middle income countries. Energy Policy 38:4422–4428CrossRefGoogle Scholar
  35. Porter ME (1991) America’s green strategy. Sci Am 193–246Google Scholar
  36. Porter ME, Claas VDL (1995) Toward a new conception of the environment–competitiveness relationship. J Econ Perspect 9(4):97–118CrossRefGoogle Scholar
  37. Ren SG, Li XL, Yuan BL, Li DY, Chen XH (2016) The effects of three types of environmental regulation on eco-efficiency: a cross-region analysis in China. J Clean Prod 173:245–255CrossRefGoogle Scholar
  38. Requate T, Unold W (2003) Environmental policy incentives to adopt advanced abatement technology: Will the true ranking please stand up? Eur Econ Rev 47(1):125–146CrossRefGoogle Scholar
  39. Salim RA, Shafiei S (2014) Urbanization and renewable and non-renewable energy consumption in OECD countries: an empirical analysis. Econ Model 38:581–591CrossRefGoogle Scholar
  40. Simpson RD, Iii RLB (1996) Taxing variable cost: environmental regulation as industrial policy. J Environ Econ Manag 30(3):282–300CrossRefGoogle Scholar
  41. Sinn HW (2008) Public policies against global warming: a supply side approach. Int Tax Public Finance 15(4):360–394CrossRefGoogle Scholar
  42. Susskind LE (1989) Four important changes in the American approach to environmental regulation. Econ Ecol: Towards Sustain Dev 295–305Google Scholar
  43. Tong H (1978) On a threshold model in pattern recognition and signal processing. Sijthoff & Noordhoff, AmsterdamGoogle Scholar
  44. Wang Y, Zhao T (2015) Impacts of energy-related CO2 emissions: evidence from under developed, developing and highly developed regions in China. Ecol Indicat 50:186–195CrossRefGoogle Scholar
  45. Wang J, Zhao T, Wang YN (2016) How to achieve the 2020 and 2030 emissions targets of China: evidence from high, mid and low energy-consumption industrial sub-sectors. Atmos Environ 145:280–292CrossRefGoogle Scholar
  46. Wang W, Li M, Zhang M (2017a) Study on the changes of the decoupling indicator between energy-related CO2, emission and GDP in China. Energy 128:11–18CrossRefGoogle Scholar
  47. Wang Y, Kang Y, Wang J, Xu L (2017b) Panel estimation for the impacts of population-related factors on CO2 emissions: a regional analysis in China. Ecol Indic 78:322–330CrossRefGoogle Scholar
  48. Wang Y, Chen W, Kang Y et al (2018a) Spatial correlation of factors affecting CO2 emission at provincial level in China: a geographically weighted regression approach. J Clean Prod 184:929–937CrossRefGoogle Scholar
  49. Wang Y, Zhao M, Chen W (2018b) Spatial effect of factors affecting household CO2 emissions at provincial level in China: a geographically weighted regression model. Carbon Manag 9(2):187–200CrossRefGoogle Scholar
  50. Xu YZ, Yang YC, Guo J (2015) The paths and effects of environmental regulation on China’s carbon emissions: an empirical study based on Chinese provincial data. Sci Sci Manag S&T 36(10):135–146 (in Chinese) Google Scholar
  51. Yang YY, Zhao T, Wang Y, Shi Z (2015) Research on impacts of population-related factors on carbon emissions in Beijing from 1984 to 2012. Environ Impact Assess Rev 55:45–53CrossRefGoogle Scholar
  52. Zhang XF, Han X, Wu JJ (2014) Relationship between environmental regulation and carbon emission: reverse effect or regressive effect—Based on provincial panel data from 2000 to 2010. Soft Sci 28(07):136–139+144 (in Chinese) Google Scholar
  53. Zhao X, Yin H, Zhao Y (2015) Impact of environmental regulations on the efficiency and CO2, emissions of power plants in China. Appl Energy 149:238–247CrossRefGoogle Scholar
  54. Zhao LT, Zhao T, Wang Y (2017) A multisectoral decomposition analysis of Beijing carbon emissions. Clean Technol Environ Policy 19:565–575CrossRefGoogle Scholar
  55. Zhou Y, Zhu S, He C (2017) How do environmental regulations affect industrial dynamics? Evidence from China’s pollution-intensive industries. Habitat Int 60:10–18CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Yanan Wang
    • 1
  • Yihui Zuo
    • 1
  • Wei Li
    • 2
  • Yanqing Kang
    • 3
  • Wei Chen
    • 1
  • Minjuan Zhao
    • 1
  • Haibin Chen
    • 1
  1. 1.College of Economics and ManagementNorthwest A&F UniversityYanglingChina
  2. 2.College of Management and EconomicsTianjin UniversityTianjinChina
  3. 3.College of Administrative EngineeringZhengzhou UniversityZhengzhouChina

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