A novel dataset of emission abatement sector extended input-output table for environmental policy analysis
Introduction
The rapid economic growth in China is based on the sacrifice of environmental quality. Multiple air emissions in China all come to top around the world, including but not limited to air pollution such as carbon dioxide (CO2), sulfur dioxide (SO2), oxides of nitrogen (NOx) and soot and dust (SD) [1], [2], [3]. Besides, the problem of water pollution is receiving increasing attention in China, especially chemical oxygen demand (COD), ammonia nitrogen (AN) and heavy metal pollutions [4], [5]. Because air and water pollutions have severe environmental and health impacts [6], the Chinese government has realized the seriousness of its environmental problem, and has issued a series of environmental laws and regulations in recent two decades [7], [8].
Considering the trade-offs between economy and environment, environmental policy will have impacts not only on environment, but also on economy [9], [10], [11], [12]. To be specific, emission and abatement condition under the influences of environmental policy will impact production strategies directly or indirectly. Besides, environmental benefit arising from high intensity abatement activities will lead to abatement cost and economic loss. In fact, from the enterprise or sector perspective, strategies of production, abatement and emission are mutually restrictive. Moreover, the production scale of one sector is influenced by other sectors.
In order to support the trade-offs analysis between economy and environment and the cost-benefit analysis of environmental policy at sector level, a novel dataset of extended input-output table is proposed in this study. Input-output table gives quantitative description of intersectoral relationships in an economic structure [13]. The conventional input-output table is improved by introducing various emission abatement sectors. Thus, the emission abatement sector extended input-output table (EAS-IOT) is constructed. The emission load and abatement cost, as well as abatement benefit of each production sector can be monetarily evaluated through emission abatement sectors in the extended input-output dataset. Additionally, a case study of Beijing is provided. Firstly, we compare the environmental efficiencies based on the new framework and the conventional method. It is indicated that the new framework reduces the biased estimation related to the conventional IOT. Secondly, we assess the effects of different environmental policies on economy and emission. It can be noted that raising environmental tax rate has positive effects on environmental efficiency and emission intensity.
The dataset of EAS-IOT has insights into the following aspects: (a) It integrates production and abatement activities at input-output database level; (b) It quantifies cost and benefit monetarily associated with abatement activities; (c) It depicts the value flows among internal production sectors, internal emission abatement sectors, and interactional production and abatement sectors; (d) It could be extended by multiple pollutions if associate data are available (such as air pollution, water pollution, solid waste pollution); (e) It is capable of supporting policy analysis at different levels such as national level, provincial level, and regional level.
The EAS-IOT can be applied in many fields: (a) the assessment of the impacts of environmental policy both on economy and on environment, (b) the estimation of environmental efficiency and abatement welfare by combining with optimization model, (c) the evaluation of efficiency and productivity changes of an economy’s production and abatement activities.
Section snippets
Environmentally extended input-output analysis
There are massive studies for analyzing the impacts of environmental policy on economy and environment with various methods. For instance, Igos et al. predicted the environmental effects of energy policies using a hybrid analysis combined life cycle analysis (LCA) and input-output model [14]. Sommer et al. used the econometric input-output Dynamic New Keynesian (DYNK) model to analyze private consumption and distributional impacts on different household income quintiles in Austria [15].
The emission abatement sector extended input-output table (EAS-IOT)
In view of the trade-offs between economy and environment, we extend the conventional Chinese IOTs with environmental part. Besides, in order to monetarily quantify the material flow between production sectors and emission abatement sectors, and to extract the costs and benefits associated with emission abatement activities, we introduce various emission abatement sectors into the conventional Chinese IOTs as the environmental part. This section gives description of the extension procedure and
Case study: environmental efficiency evaluation and environmental policy analysis for Beijing
In this section, we use the EAS-IOT and frontier based environmentally extended input-output model to estimate environmental efficiency and assess effects of environmental policy for Beijing in 2012 for demonstration purposes.
Conclusion
In this study, we introduce a novel framework about the input-output table (IOT) extension at dataset level. The framework provides a solution to researches on trade-offs analysis and cost-benefit analysis of economy and environment. The conventional IOT and environmentally extended input-output table (EEIOT) cannot take emission abatement into consideration. Emission abatement sector extended input-output table (EAS-IOT) plays a key role in filling this gap. The EAS-IOT, compared to
Acknowledgement
We gratefully acknowledge the financial support from the National Natural Science Foundation of China (Grant Nos. 71871022, 71471018, 71521002 and 71828401), the Fok Ying Tung Education Foundation (Grant No. 161076), the Social Science Foundation of Beijing (Grant No. 16JDGLB013), the Joint Development Program of Beijing Municipal Commission of Education, the International Clean Energy Talent Program of Chinese Scholarship Council, and the National Key R&D Program (Grant No. 2016YFA0602603).
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