What matters for carbon emissions in regional sectors? A China study of extended STIRPAT model

Yang, Lixia; Xia, Hao; Zhang, Xiaoling; Yuan, Shengli

doi:10.1016/j.jclepro.2018.01.116

Cited by 178 publications

(83 citation statements)

References 46 publications

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“…We used urban population density (POP) in each province at the end of the year to represent P. We did not use the total population density in the region because the construction industry is mainly located in urban areas. According to the previous studies [43,44], we selected GDP per capita (PGDP) to represent A. We selected several factors to represent T. According to Dietz and Rosa [42], T is not a single factor but comprises many separate factors that influence the environment.…”

Section: Spatial Panel Econometric Modelmentioning

confidence: 99%

“…In a few studies, T was interpreted as the residual term [45,46]. In some other studies [44,47,48], T may be represented by multiple different variables, including energy intensity, energy structure, urbanization, and industrialization. In more recent studies, the stock of technical patents associated with carbon emissions was also used to represent T [49][50][51].…”

Section: Spatial Panel Econometric Modelmentioning

confidence: 99%

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The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

et al. 2020

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As the largest carbon emitter in the world, China is confronted with great challenges of mitigating carbon emissions, especially from its construction industry. Yet, the understanding of carbon emissions in the construction industry remains limited. As one of the first few attempts, this paper contributes to the literature by identifying the determinants of carbon emissions in the Chinese construction industry from the perspective of spatial spillover effects. A panel dataset of 30 provinces or municipalities from 2005 to 2015 was used for the analysis. We found that there is a significant and positive spatial autocorrelation of carbon emissions. The local Moran’s I showed local agglomeration characteristics of H-H (high-high) and L-L (low-low). The indicators of population density, economic growth, energy structure, and industrial structure had either direct or indirect effects on carbon emissions. In particular, we found that low-carbon technology innovation significantly reduces carbon emissions, both in local and neighboring regions. We also found that the industry agglomeration significantly increases carbon emissions in the local regions. Our results imply that the Chinese government can reduce carbon emissions by encouraging low-carbon technology innovations. Meanwhile, our results also highlight the negative environmental impacts of the current policies to promote industry agglomeration.

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Section: Spatial Panel Econometric Modelmentioning

confidence: 99%

Section: Spatial Panel Econometric Modelmentioning

confidence: 99%

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

et al. 2020

View full text Add to dashboard Cite

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“…where I represents the environmental impact, P is the population, A is the economic development, T is the technical level, α is the model coefficient, b, c, d are the index of independent variables, e is the model error term. According to the relevant research results [19][20][21], based on the concept of residual-free decomposition of Kaya identity established by Yoichi Kaya, this study decomposes the influencing factors under the framework of IPAT model, which can be expressed as follow:…”

Section: Stirpat Model Extensionmentioning

confidence: 99%

Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

et al. 2020

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Reducing CO2 emissions of industrial energy consumption plays a significant role in achieving the goal of CO2 emissions peak and decreasing total CO2 emissions in northeast China. This study proposed an extended STIRPAT model to predict CO2 emissions peak of industrial energy consumption in Jilin Province under the four scenarios (baseline scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS), and low-carbon scenario (LCS)). We analyze the influences of various factors on the peak time and values of CO2 emissions and explore the reduction path and mechanism to achieve CO2 emissions peak in industrial energy consumption. The results show that the peak time of the four scenarios is respectively 2026, 2030, 2035 and 2043, and the peak values are separately 147.87 million tons, 16.94 million tons, 190.89 million tons and 22.973 million tons. Due to conforming to the general disciplines of industrial development, the result in ELS is selected as the optimal scenario. The impact degrees of various factors on the peak value are listed as industrial CO2 emissions efficiency of energy consumption > industrialized rate > GDP > urbanization rate > industrial energy intensity > the share of renewable energy consumption. But not all factors affect the peak time. Only two factors including industrial clean-coal and low-carbon technology and industrialized rate do effect on the peak time. Clean coal technology, low carbon technology and industrial restructuring have become inevitable choices to peak ahead of time. However, developing clean coal and low-carbon technologies, adjusting the industrial structure, promoting the upgrading of the industrial structure and reducing the growth rate of industrialization can effectively reduce the peak value. Then, the pathway and mechanism to reducing industrial carbon emissions were proposed under different scenarios. The approach and the pathway and mechanism are expected to offer better decision support to targeted carbon emission peak in northeast of China.

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“…Manufacturers' choices of green innovation mode affect not only their own performance, but also the likelihood of achieving emission reduction goals set by governments. For instance, the Chinese government has set a target of 60% to 65% of CO 2 emission reduction per unit of GDP by 2030 [16,17]. However, the high risk of significant R&D costs, knowledge spillover, and the positive externality of green innovations are all likely to result in manufacturers' hesitation toward developing and adopting radical green innovations [18].…”

Section: Introductionmentioning

confidence: 99%

Green Innovation Mode under Carbon Tax and Innovation Subsidy: An Evolutionary Game Analysis for Portfolio Policies

Zhang

Zhu

et al. 2020

Sustainability

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Previous literature has shown that manufacturers’ choices between radical and incremental green innovation modes can greatly impact the tradeoff between industry growth and carbon emission reduction. Yet, how the government can motivate manufacturers to implement radical green innovations to reduce carbon emission is unclear. In this paper, the researchers construct an evolutionary game model to analyze the joint impacts of carbon tax and innovation subsidy on manufacturers’ choices of green innovation mode. We derive the conditions for manufacturers’ stable strategies. Based on those results, we find that four factors—carbon tax, innovation subsidy, consumer green preference, and manufacturers’ capabilities of absorbing and adopting new technologies—may facilitate the choice of radical innovation. Furthermore, we conduct numerical simulations to verify the theoretical results, and further illustrate how the synergy of carbon tax rate and subsidy level affects the evolution of the green innovation mode choices. Specifically, we demonstrate the superiority of portfolio policy in the early stage of green innovation over single policy. In contrast, in the later stage, it is carbon tax but not innovation subsidy that remains effective. We discuss the insights for the government to formulate appropriate environmental policies to effectively promote the adoption of green innovation and reduce carbon emission.

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What matters for carbon emissions in regional sectors? A China study of extended STIRPAT model

Cited by 178 publications

References 46 publications

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

The Determinants of Carbon Emissions in the Chinese Construction Industry: A Spatial Analysis

Peaking Industrial Energy-Related CO2 Emissions in Typical Transformation Region: Paths and Mechanism

Green Innovation Mode under Carbon Tax and Innovation Subsidy: An Evolutionary Game Analysis for Portfolio Policies

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