China’s environmental policy intensity for 1978–2019

Scientific data - Tập 9 Số 1
Guoxing Zhang1, Yang Gao1, Jiexun Li2, Bin Su3, Zhanglei Chen1, Weichun Lin1
1School of Management, Lanzhou University, Lanzhou 730000, China
2Department of Decision Sciences, Western Washington University, Bellingham, WA 98225, USA
3Energy Studies Institute, National University of Singapore, Singapore, 119620, Singapore

Tóm tắt

AbstractImproving the measurement of environmental policy intensity would affect not only the selection of variables in environmental policy research but also the research conclusions when evaluating policy effects. Because direct evaluation is lacking, the existing research usually applies data such as pollutant emission data, or the number of policies to construct proxy variables. However, these proxy variables are affected by many assumptions and different selection criteria, and they are inevitably accompanied by endogeneity problems. In this study, China’s environmental policy is comprehensively collected for the first time, and a machine learning algorithm is applied to evaluate the policy intensity. We provide all the policies issued by the Chinese government from 1978 to 2019 and the quantified intensity for each policy. We also distinguish all policies into three types according to their attributes. This dataset can help researchers to further understand China’s environmental policy system. In addition, it provides a valuable dataset for related research on evaluating environmental policy and recommending actions for further improvement.

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Tài liệu tham khảo

Zhou, Q., Zhang, X., Shao, Q. & Wang, X. The non-linear effect of environmental regulation on haze pollution: Empirical evidence for 277 Chinese cities during 2002–2010. Journal of Environmental Management 248, 109274, https://doi.org/10.1016/j.jenvman.2019.109274 (2019).

Duan, H. et al. Assessing China’s efforts to pursue the 1.5 °C warming limit. Science 372, 378–385, https://doi.org/10.1126/science.aba8767 (2021).

Mo, J. et al. The role of national carbon pricing in phasing out China’s coal power. iScience 24, 102655, https://doi.org/10.1016/j.isci.2021.102655 (2021).

May, P. J. Policy design and implementation. In Handbook of public administration 223, 233 (2003).

Howlett, M. & Cashore, B. The Dependent Variable Problem in the Study of Policy Change: Understanding Policy Change as a Methodological Problem. Journal of Comparative Policy Analysis: Research and Practice 11, 33–46, https://doi.org/10.1080/13876980802648144 (2009).

Jones, B. D. & Baumgartner, F. R. From There to Here: Punctuated Equilibrium to the General Punctuation Thesis to a Theory of Government Information Processing. Policy Studies Journal 40, 1–20, https://doi.org/10.1111/j.1541-0072.2011.00431.x (2012).

Jahn, D. & Kuitto, K. Taking stock of policy performance in Central and Eastern Europe: Policy outcomes between policy reform, transitional pressure and international influence. European Journal of Political Research 50, 719–748, https://doi.org/10.1111/j.1475-6765.2010.01981.x (2011).

Knill, C., Schulze, K. & Tosun, J. Regulatory policy outputs and impacts: Exploring a complex relationship. Regulation & Governance 6, 427–444, https://doi.org/10.1111/j.1748-5991.2012.01150.x (2012).

Schaffrin, A., Sewerin, S. & Seubert, S. Toward a Comparative Measure of Climate Policy Output. Policy Studies Journal 43, 257–282, https://doi.org/10.1111/psj.12095 (2015).

Schmidt, T. S. & Sewerin, S. Measuring the temporal dynamics of policy mixes – An empirical analysis of renewable energy policy mixes’ balance and design features in nine countries. Research Policy 48, 103557, https://doi.org/10.1016/j.respol.2018.03.012 (2019).

Carley, S. & Miller, C. J. Regulatory Stringency and Policy Drivers: A Reassessment of Renewable Portfolio Standards. Policy Studies Journal 40, 730–756, https://doi.org/10.1111/j.1541-0072.2012.00471.x (2012).

Clinton, J. D. & Lapinski, J. S. Measuring Legislative Accomplishment, 1877–1994. American Journal of Political Science 50, 232–249, https://doi.org/10.1111/j.1540-5907.2006.00181.x (2006).

Grant, J. T. & Kelly, N. J. Legislative Productivity of the U.S. Congress, 1789–2004. Political Analysis 16, 303–323, https://doi.org/10.1093/pan/mpm035 (2008).

Botta, E. & Koźluk, T. Measuring Environmental Policy Stringency in OECD Countries. https://doi.org/10.1787/5jxrjnc45gvg-en (2014).

OECD. Environmental Policy Stringency index (Edition 2017). https://doi.org/10.1787/b4f0fdcc-en (2018).

OECD. How stringent are environmental policies?, https://www.oecd.org/economy/greeneco/how-stringent-are-environmental-policies.htm (2016).

Debrun, X., Moulin, L., Turrini, A., Ayuso-i-Casals, J. & Kumar, M. S. Tied to the mast? National fiscal rules in the European Union. Economic Policy 23, 298–362, https://doi.org/10.1111/j.1468-0327.2008.00199.x (2008).

Combes, J. L., Debrun, X., Minea, A. & Tapsoba, R. Inflation Targeting, Fiscal Rules and the Policy Mix: Cross‐effects and Interactions. The Economic Journal 128, 2755–2784, https://doi.org/10.1111/ecoj.12538 (2018).

Du, W. & Li, M. Assessing the impact of environmental regulation on pollution abatement and collaborative emissions reduction: Micro-evidence from Chinese industrial enterprises. Environmental Impact Assessment Review 82, 106382, https://doi.org/10.1016/j.eiar.2020.106382 (2020).

Zhao, J., Jiang, Q., Dong, X. & Dong, K. Would environmental regulation improve the greenhouse gas benefits of natural gas use? A Chinese case study. Energy Economics 87, 104712, https://doi.org/10.1016/j.eneco.2020.104712 (2020).

Zhou, Q., Song, Y., Wan, N. & Zhang, X. Non-linear effects of environmental regulation and innovation – Spatial interaction evidence from the Yangtze River Delta in China. Environmental Science & Policy 114, 263–274, https://doi.org/10.1016/j.envsci.2020.08.006 (2020).

Neumayer, E. Are left-wing party strength and corporatism good for the environment? Evidence from panel analysis of air pollution in OECD countries. Ecological Economics 45, 203–220, https://doi.org/10.1016/S0921-8009(03)00012-0 (2003).

Bernauer, T. & Koubi, V. Effects of political institutions on air quality. Ecological Economics 68, 1355–1365, https://doi.org/10.1016/j.ecolecon.2008.09.003 (2009).

Liefferink, D., Arts, B., Kamstra, J. & Ooijevaar, J. Leaders and laggards in environmental policy: a quantitative analysis of domestic policy outputs. Journal of European Public Policy 16, 677–700, https://doi.org/10.1080/13501760902983283 (2009).

Zhang, G., Zhang, P., Zhang, Z. G. & Li, J. Impact of environmental regulations on industrial structure upgrading: An empirical study on Beijing-Tianjin-Hebei region in China. Journal of Cleaner Production 238, 117848, https://doi.org/10.1016/j.jclepro.2019.117848 (2019).

Zhang, G., Deng, N., Mou, H., Zhang, Z. G. & Chen, X. The impact of the policy and behavior of public participation on environmental governance performance: Empirical analysis based on provincial panel data in China. Energy Policy 129, 1347–1354, https://doi.org/10.1016/j.enpol.2019.03.030 (2019).

Tang, M., Li, X., Zhang, Y., Wu, Y. & Wu, B. From command-and-control to market-based environmental policies: Optimal transition timing and China’s heterogeneous environmental effectiveness. Economic Modelling 90, 1–10, https://doi.org/10.1016/j.econmod.2020.04.021 (2020).

Guo, R. & Yuan, Y. Different types of environmental regulations and heterogeneous influence on energy efficiency in the industrial sector: Evidence from Chinese provincial data. Energy Policy 145, 111747, https://doi.org/10.1016/j.enpol.2020.111747 (2020).

Mou, H., Atkinson, M. M. & Tapp, S. Do Balanced Budget Laws Matter in Recessions? Public Budgeting & Finance 38, 28–46, https://doi.org/10.1111/pbaf.12163 (2018).

Huang, C. Quantitative Research on Policy Literature. (Science Press, 2016).

Huang, C. et al. A bibliometric study of China’s science and technology policies: 1949–2010. Scientometrics 102, 1521–1539, https://doi.org/10.1007/s11192-014-1406-4 (2015).

Sheng, J., Zhou, W. & Zhu, B. The coordination of stakeholder interests in environmental regulation: Lessons from China’s environmental regulation policies from the perspective of the evolutionary game theory. Journal of Cleaner Production 249, 119385, https://doi.org/10.1016/j.jclepro.2019.119385 (2020).

Sheng, J. & Webber, M. Incentive-compatible payments for watershed services along the Eastern Route of China’s South-North Water Transfer Project. Ecosystem Services 25, 213–226, https://doi.org/10.1016/j.ecoser.2017.04.006 (2017).

World Bank. Five Years after Rio Innovations in Environmental Policy. (1997).

Xie, R.-h, Yuan, Y.-j & Huang, J.-J. Different Types of Environmental Regulations and Heterogeneous Influence on “Green” Productivity: Evidence from China. Ecological Economics 132, 104–112, https://doi.org/10.1016/j.ecolecon.2016.10.019 (2017).

Li, H.-l, Zhu, X.-h, Chen, J.-y & Jiang, F.-t Environmental regulations, environmental governance efficiency and the green transformation of China’s iron and steel enterprises. Ecological Economics 165, 106397, https://doi.org/10.1016/j.ecolecon.2019.106397 (2019).

Berman, E. & Bui, L. T. M. Environmental Regulation and Productivity: Evidence from Oil Refineries. The Review of Economics and Statistics 83, 498–510, https://doi.org/10.1162/00346530152480144 (2001).

Ryan, S. P. The Costs of Environmental Regulation in a Concentrated Industry. Econometrica 80, 1019–1061, https://doi.org/10.3982/ECTA6750 (2012).

Acemoglu, D., Aghion, P., Bursztyn, L. & Hemous, D. The Environment and Directed Technical Change. American Economic Review 102, 131–166, https://doi.org/10.1257/aer.102.1.131 (2012).

Acemoglu, D., Akcigit, U., Hanley, D. & Kerr, W. Transition to Clean Technology. Journal of Political Economy 124, 52–104, https://doi.org/10.1086/684511 (2016).

Aghion, P., Dechezleprêtre, A., Hémous, D., Martin, R. & Van Reenen, J. Carbon Taxes, Path Dependency, and Directed Technical Change: Evidence from the Auto Industry. Journal of Political Economy 124, 1–51, https://doi.org/10.1086/684581 (2016).

Sun, L., Zhu, D. & Chan, E. H. Public participation impact on environment NIMBY conflict and environmental conflict management: Comparative analysis in Shanghai and Hong Kong. Land use policy 58, 208–217, https://doi.org/10.1016/j.landusepol.2016.07.025 (2016).

Jegadeesh, N. & Wu, D. Word power: A new approach for content analysis. Journal of Financial Economics 110, 712–729, https://doi.org/10.1016/j.jfineco.2013.08.018 (2013).

Loughran, T. I. M. & McDonald, B. When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks. The Journal of Finance 66, 35–65, https://doi.org/10.1111/j.1540-6261.2010.01625.x (2011).

Baker, S. R., Bloom, N. & Davis, S. J. Measuring Economic Policy Uncertainty*. The Quarterly Journal of Economics 131, 1593–1636, https://doi.org/10.1093/qje/qjw024 (2016).

Gentzkow, M., Kelly, B. & Taddy, M. Text as Data. Journal of Economic Literature 57, 535–574, https://doi.org/10.1257/jel.20181020 (2019).

Loughran, T., McDonald, B. & Yun, H. A Wolf in Sheep’s Clothing: The Use of Ethics-Related Terms in 10-K Reports. Journal of Business Ethics 89, 39–49, https://doi.org/10.1007/s10551-008-9910-1 (2009).

Malhotra, S., Reus, T. H., Zhu, P. & Roelofsen, E. M. The Acquisitive Nature of Extraverted CEOs. Administrative Science Quarterly 63, 370–408, https://doi.org/10.1177/0001839217712240 (2017).

Gamache, D. L., McNamara, G., Mannor, M. J. & Johnson, R. E. Motivated to Acquire? The Impact of CEO Regulatory Focus on Firm Acquisitions. Academy of Management Journal 58, 1261–1282, https://doi.org/10.5465/amj.2013.0377 (2014).

Park, G. et al. Automatic personality assessment through social media language. Journal of Personality and Social Psychology 108, 934, https://doi.org/10.1037/pspp0000020 (2015).

Harrison, J. S., Thurgood, G. R., Boivie, S. & Pfarrer, M. D. Measuring CEO personality: Developing, validating, and testing a linguistic tool. Strategic Management Journal 40, 1316–1330, https://doi.org/10.1002/smj.3023 (2019).

Storm, H., Baylis, K. & Heckelei, T. Machine learning in agricultural and applied economics. European Review of Agricultural Economics 47, 849–892, https://doi.org/10.1093/erae/jbz033 (2020).

Athey, S. & Imbens, G. W. Machine Learning Methods That Economists Should Know About. Annual Review of Economics 11, 685–725, https://doi.org/10.1146/annurev-economics-080217-053433 (2019).

Zhang, G. China’s environmental policy intensity for 1978–2019, figshare, https://doi.org/10.6084/m9.figshare.16740376.v1 (2022).

Biecek, P. & Burzykowski, T. Explanatory Model Analysis, 2020.

Bai, S., Qi, H.-D. & Xiu, N. Constrained Best Euclidean Distance Embedding on a Sphere: A Matrix Optimization Approach. SIAM Journal on Optimization 25, 439–467, https://doi.org/10.1137/13094918X (2015).

Berthold, M. & Höppner, F. On Clustering Time Series Using Euclidean Distance and Pearson Correlation. arXiv preprint arXiv:1601.02213 (2016).