A comprehensive city-level final energy consumption dataset including renewable energy for China, 2005-2021.

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China's city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China's city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes.

Multidimensional Energy Poverty in China: Measurement and Spatio-Temporal Disparities Characteristics.

As the world's most populous country, China's energy poverty reduction achievements directly impact the global energy poverty reduction process. Analyzing energy poverty in China is therefore critical to consolidating the results of poverty eradication, eliminating relative poverty, and improving the social welfare of residents. However, prior research neither considered the applicability of existing energy poverty indicators to the current Chinese reality, nor the spatiotemporal disparities of energy poverty using micro-level data. To study the dynamics of energy poverty in China at the household level, a new multidimensional energy poverty index is constructed with seven dimensions using multiple correspondence analysis methods. Furthermore, provincial disparities and characteristics of energy poverty are compared using a spatial autocorrelation analysis method. The findings show that energy poverty has improved in China from 2012 to 2018, but its incidence and intensity remain high. Moreover, significant regional differences in energy poverty exist between different regions of China. High levels of energy poverty are mainly concentrated in the western and northeastern regions (especially in rural areas), and the urban-rural gap shows a similar pattern. The results obtained from spatial autocorrelation analysis demonstrate that China's energy poverty exhibits significant spatial clustering characteristics. Further, the results of standard deviation ellipse show that during the study period, the center of gravity of energy poverty in China was in Henan province and gradually shifted to the northwest. These findings help policymakers to formulate specific energy poverty reduction policies for various groups affected by energy poverty.

Probabilistic carbon price prediction with quantile temporal convolutional network considering uncertain factors.

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict.

China's ambitious low-carbon goals require fostering city-level renewable energy transitions.

Cities in China, as elsewhere, are increasingly playing a crucial role in mitigating climate change. We developed a panel dataset on renewable energy transition in Chinese cities, and assessed the CO2 emissions reduction of city-level renewable energy transition. We found that city-level renewable energy transition only reduced 446 million tonnes of CO2 emissions from 2005 to 2019. Moreover, the 2030 carbon peak target will be missed in the business-as-usual scenario. The CO2 emissions reduction of city-level renewable energy transition will significantly increase in the policy constraint scenario and in the technology breakthrough scenario, and the 2030 carbon peak target will likely be reached in both these scenarios, with a range of possible CO2 emissions in 2030 equal to 8.34-10.43 and 8.00-10.07 billion tonnes, respectively. In this study, we were the first to assess the historical contribution and prospective trajectory of CO2 emissions reduction of China's city-level renewable energy transition.

How does biased technological progress affect haze pollution? Evidence from APEC economies.

Biased technological progress is the act of energy conservation and emission reduction by changing the marginal rate of substitution. In this study, we introduced renewable energy into a production function, and proposed a method of identifying biased characteristics of technological progress, based on marginal productivity theory. A panel dataset for the Asia-Pacific Economic Cooperation (APEC) economies from 2000 to 2017 was analyzed to explore the effect of biased technological progress in reducing particulate matter (PM2.5). We found that input biased technological progress tended to use more non-renewable energy. Input biased technological progress aggravated haze pollution; however, this effect decreased as the PM2.5 concentration increased. Output biased technological progress significantly reduced haze pollution in high-income economies, but increased it in low-income economies. The effect of neutral technological progress on haze pollution was the opposite of the effect from output biased technological progress. We also found that increasing renewable energy consumption and reducing energy intensity were separate effective paths for input and output biased technological progress, respectively, to mitigate haze pollution. For neutral technological progress, improving total factor productivity was an important way to mitigate haze pollution. Finally, several policy recommendations are proposed to mitigate haze pollution in APEC economies.

Does environment-biased technological progress reduce CO2 emissions in APEC economies? Evidence from fossil and clean energy consumption.

Environment-biased technological progress plays a critical role in carbon reduction, while the association among environment-biased technological progress, energy consumption, and carbon emissions has not been paid enough attention. Working with a unique spatial panel dataset of APEC economies spanning the 2000-2017 period, we employed the nonspatial panel model and the spatial panel model to investigate the role of fossil energy (FE) and clean energy (CE) consumption in carbon dioxide (CO2) abatement through environment-biased technological progress (EBTP). We decomposed EBTP into both emission-reducing biased technological progress (ErBTP) and energy-saving biased technological progress (EsBTP). The results show that the direct effect of EBTP on CO2 emissions was significantly negative and that the direct effect of ErBTP was significantly larger than that of EsBTP. EBTP reduced CO2 emissions through CE consumption, whereas it increased CO2 emissions through FE consumption, that is, EBTP had a "backfire effect" on FE consumption. More into detail, ErBTP had a larger effect on CO2 emissions in developing economies, while EsBTP played a more important role in developed economies. Furthermore, the results of the robustness test were consistent with our findings. Finally, several policy options were suggested to reduce CO2 emissions in APEC economies.

Decomposition of CO2 emission intensity in Chinese MIs through a development mode extended LMDI method combined with a production-theoretical approach.

An improved understanding of the influence of development mode on carbon intensity (CI) can aid China's manufacturing industries (MIs) to reduce CI without affecting the development of Chinese MIs. To demonstrate the relationship between the development mode and CI well, this work provides the following contributions: (a) The driving factors of Chinese MIs' CI are decomposed by a development mode extended Logarithmic Mean Divisia Index method (LMDI)); (b) The impact of the factors of industrial development mode is analyzed by comprehensively by the extended LMDI combined with production -theoretical decomposition analysis (PDA). (c) The disparities of the driving factors in different industrial classifications are evaluated by applying the development mode extended LMDI combined with PDA in Chinese MIs between 2000 and 2015. Results showed that, (1) at the entire MIs' level, the effects of ratio of energy consumption to R&D (RI) and potential carbon intensity (PCI) were the two leading contributors to the CI decline in Chinese MIs. (2) In terms of the cumulative effects at the subsectoral level, PCI had the largest curbing effect on the CI of all subsectors, and investment intensity (II) had the greatest stimulating effect on the CI of all subsectors. (3) Among the three industrial classifications, the middle-end MIs experienced the largest carbon intensity decline from the 10th Five-Year Plan (FYP) to the 12th FYP, followed by the low- and high-end MIs. RI and PCI decreased the CI of Chinese MIs in all industrial classifications and economic development stages. Potential energy efficiency and II were the two major contributors to CI improvement in all industrial classifications and economic development stages.

Effects of a generalized dual-credit system on green technology investments and pricing decisions in a supply chain.

China recently implemented a corporate average fuel consumption regulation and new-energy vehicle credit program (dual-credit system) to prompt the transition to new-energy vehicles. This study generalizes the dual-credit system (energy credit and green credit) and investigates its effects on the green technology investments (GTI) and pricing decisions in a two-echelon supply chain consisting of three possible scenarios, Case O (conventional product only), Case B (both conventional and green products), and Case G (green product only). The obtained results show that the GTI made by manufacturers follow high threshold and low threshold. The generalized dual-credit system increases both thresholds and promotes the transition from Case O to Case B and Case B to Case G. The transition is sensitive to standard energy consumption per-unit (SECP), green credit quota (GCQ), and price of green credit (PGC). The generalized dual-credit system benefits the manufacturers who exceed the low threshold, vice versa, especially for whose conventional product with lower initial energy consumption per unit. The generalized dual-credit system contributes to GTI and environment effects in all cases. But, the impacts on GTI, environment effects, and profit differ in sensitivity to SECP, GCQ, and PGC in different cases. Numerical simulation is given and all the proofs are shown in appendix.

Would an increasing block carbon tax be better? A comparative study within the Stackelberg Game framework.

Carbon tax is a policy for controlling carbon emissions, and is widely used worldwide. However, a non-differentiated carbon tax increases financial burden on manufacturers and inhibits their willingness to produce. Herein, a novel carbon tax policy involving an increasing block carbon tax is proposed, and the policy's possible implementation effects are analyzed. First, based on the Stackelberg game, the study constructs a social welfare model considering carbon emissions. Then, the study discusses the theoretical characteristics of the proposed carbon tax policy. After that, the differences and similarities between a flat carbon tax and an increasing block carbon tax are analyzed using a numerical simulation. The results indicate that: (1) compared to flat carbon tax, an increasing block carbon tax has the same controlling effect on carbon emissions. Both forms of taxes can restrict total carbon emissions within the desired range. (2) An increasing block carbon tax policy can significantly reduce tax burdens for manufacturers, and encourages low-carbon production. (3) An increasing block carbon tax can flexibly adjust the relationship between government's carbon tax revenue and manufacturer's tax burden. Finally, some policy implications for the proposed strategy are revealed.

An event study analysis of price adjustment of refined oil and air quality in China.

Since marketization of the refined oil price, the Chinese government has used refined oil price adjustments to control air pollution. Using an event study analysis, we examine whether these price adjustments have impacted air quality. We test the abnormal returns of 12 price adjustments between 2014 and 2015 in 51 major cities of China. The results show that the impact on air quality of refined oil price decreases is larger than the impact of oil price increases. Although results indicate air quality has deteriorated, the impact is insignificant for most of the cities. Consequently, we conclude that price suspension of refined oil has had a negligible impact on air quality. This policy is not a viable method to improve the air quality in the short run.