Abstract
Carbon policies can be expected to increase the price of fossil fuel, either directly through a cap-and-trade system or carbon tax, or indirectly by regulations that place an implicit tax on fossil fuel. We construct a theoretical model to decompose the effect on employment in a specific industry caused by an increase in the price of fossil fuel. We find that the total effect is determined by the market’s responsiveness to the changes of the prices of fossil fuel and final product. We verify the theoretical model by an empirical analysis of China’s thermal power industry. Because China is the largest carbon emitter in the world and thermal power industry is the biggest carbon emitter industry in China, the empirical model has strong reference value for other countries and industries. Policy makers should be prepared to mitigate any adverse effects on employment that a carbon policy might cause.
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Acknowledgements
We gratefully acknowledge Dalhousie University for the financial support. Jinying Zhang acknowledges financial support from China Scholarship Council (Reward No. 201802515003) and the Humanities and Social Sciences Research Projects (Reward No. J15WG03) of Shandong Provincial Education Department. Ms. Shidan Gai, a postgraduate student at Shandong University of Finance and Economics, compiled and organized the data used in this paper. We gratefully acknowledge Ms Gai for her assistance.
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China Scholarship Council (201802515003). Education Department of Shandong Province (J15WJ03). Dalhousie University (Joint Projet 2016)
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Neither Shandong University of Finance and Economics nor Dalhousie University played any role in the design of this study nor in the collection, analysis, and interpretation of data nor in the writing of this paper. Neither University played any role the decision to submit this paper for publication.
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We are ready for opening all the data at anytime. All the data we used are publicly available. The average on-grid price and thermal coal price data for some years are from the database of Wind Characteristics, the Annual Report on Power Regulation by the State Electricity Regulatory Commission, and the National electricity price Supervision Bulletin by the National Energy Administration. All remaining data are from the China Statistical Yearbooks Database and publicly available at http://tongji.oversea.cnki.net/oversea/engnavi/navidefault.aspx.
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Appendix
Appendix
Derivative of Eq. (3)
We get MPZand MPL by partially differentiating Eq. (1), and substitute them into Eq. (2): \(\frac{{A\alpha Z^{\alpha - 1} L^{\beta } K^{\gamma } }}{p} = \frac{{A\beta Z^{\alpha } L^{\beta - 1} K^{\gamma } }}{w}\). After simplifying, we get Eq. (3).
Derivative of Eq. (4)
Totally differentiating Eq. (3), we get \(\frac{\partial L}{\partial p} = \frac{\beta Z}{\alpha w} + \frac{\beta p}{\alpha w} \cdot \frac{\partial Z}{\partial p} = \frac{\beta Z}{\alpha w}\left( {1 + \frac{p}{Z} \cdot \frac{\partial Z}{\partial p}} \right)\). Using \(\varepsilon_{{\mathrm{Zp}}}\) to represent \(\frac{p}{Z} \cdot \frac{\partial Z}{\partial p}\), we get Eq. (4).
Derivative of Eq. (8)
We get MPZand MPK by partially differentiating Eq. (1), and substitute them into Eq. (2): \(\frac{{A\alpha Z^{\alpha - 1} L^{\beta } K^{\gamma } }}{p} = \frac{{A\gamma Z^{\alpha } L^{\beta } K^{\gamma - 1} }}{g}\). After simplifying, we get Eq. (8).
Derivative of Eq. (10)
Differentiating (9) with respect to p, we get
Derivative of Eq. (12)
Substituting Eqs. (10) and (11) into (7), we get
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Zhang, J., Cross, M.L. Carbon policies, fossil fuel price, and the impact on employment. Clean Techn Environ Policy 22, 1085–1095 (2020). https://doi.org/10.1007/s10098-020-01850-x
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DOI: https://doi.org/10.1007/s10098-020-01850-x