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The impact of average wages on electricity consumption among urban residents in China has generated many fascinating debates for scholarly research, but only a few studies have considered the spatial spillover effect of average wages on residential electricity consumption. With the use of city-level panel data from 278 Chinese cities spanning 2005 to 2016, this preliminary study explores the impacts of the average wage on residential electricity consumption. Specifically, based on the spatial Durbin model with fixed effects, three different spatial weight matrices (the economic distance, the inverse distance, and the four nearest neighbours) are utilised to check the robustness of the results under different standards. The results show that the residential electricity consumption of each city increased during the observation period, presenting obvious spatial correlations. Secondly, the average wage of residents had a positive spatial spillover effect, which promoted the residential electricity consumption of both local and surrounding cities. Thirdly, the population density, electricity intensity, educational level of urban residents, and per capita household liquefied petroleum gas consumption in urban areas are key factors influencing residential electricity consumption. Therefore, improving the educational level of urban residents and reducing the electricity intensity can help reduce electricity consumption by residents in China. This paper also presents policy recommendations. [ABSTRACT FROM AUTHOR]

Residential electricity consumption has an important impact on China's construction of a low-carbon society. However, at present, little of the literature analyzes the influencing factors of residents' overall well-being from the perspective of micro investigation. Based on the micro mixed cross section data of the Chinese General Social Survey (CGSS), this paper empirically studies the impact of residential electricity consumption on residents' subjective well-being. In addition, in the heterogeneity analysis, we found that an increase in residential electricity consumption will improve the overall well-being of females and people with low levels of education, but it has no significant effect on males and people with high levels of education. Moreover, the increase in residential electricity consumption has improved the life satisfaction of young people and middle-aged people. Meanwhile, the increase in residential electricity consumption has a significant, positive impact on both low-income and high-income households. Further analysis shows that no nonlinear relationship exists between the increase in residents' power consumption and the improvement in life satisfaction. This paper enriches the research on residential energy and provides policy implications for the current Chinese government to save energy, reduce emissions, and improve residents' quality of life. [ABSTRACT FROM AUTHOR]

With the development of the smart grid in China, new opportunities for responsive industrial loads to participate in the provision of ancillary services (AS) will become accessible. This paper summarizes AS in China and analyzes the necessary characteristics and advantages of industrial users to provide AS according to their response mechanism. Cement manufacturing and aluminum smelter processes are selected as two representatives of responsive industrial loads. An agent-based model that includes generation, industrial user, and grid agents is proposed. Using two case studies, we analyze the integrated power management of conventional units and industrial loads in day-ahead and real-time AS scheduling based on real device parameters, price mechanisms and production data. The simulation results indicate that the participation of responsive industrial loads in the provision of AS, in China, can improve the coal consumption rate and the system-wide load factor as well as reduce the total system cost for the provision of AS significantly. [ABSTRACT FROM AUTHOR]

China's rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions. [ABSTRACT FROM AUTHOR]

Combined Cooling, Heating and Power (CCHP) systems have been widely used in different kinds of buildings to make better use of fuels because of their high overall efficiency. This paper presents a mathematical analysis of a CCHP system in comparison to a Heating, Ventilation and Air Conditioning (HVAC) system. The operation strategies following electric load (FEL), thermal load (FTL) and a hybrid electric-thermal load (FHL) are proposed and investigated in this study. Criteria, namely primary energy saving (PES), exergy efficiency (ηexergy), and CO2 emission reduction (CER) are defined to evaluate the performances of CCHP systems for a hypothetical building located in Dalian (China). The results indicate that: (1) a new mathematical foundation is established to find whether the recovered thermal energy and the amount of electricity generated by the power generation unit (PGU) are enough to provide the energy required; (2) the CCHP system does not always perform better than a HVAC system from an instantaneous perspective, especially in FTL mode; (3) the CCHP system in FEL operation mode can be seen as a suitable energy system in Dalian from the annual performance perspective. Furthermore, a sensitivity analysis is presented in order to show how the performances vary due to the changes of various technical variables. [ABSTRACT FROM AUTHOR]

Inter-provincial power transmission in China solved the problem of electricity production and consumption spatial mismatch, which also facilitated Chinese green energy transition and sustainable development. Aiming to understand the spatial patterns and main driving factors, this paper accounted the aggregate carbon intensity per electricity produced by each province, built the inter-provincial Out–Destination matrixes of carbon emission embodied in power transmission among 27 provincial areas of 2006 and 2019, and quantitatively analyzed the change in spatial patterns of carbon flow and carbon intensity difference between power inward and outward provinces. We found that the inter-provincial power transmission has led to the reduction in the national average carbon intensity per electricity produced from 0.855 kg/kWh in 2006 to 0.628 kg/kWh in 2019 and the green energy transition of all provinces. The spatial morphology characteristics of carbon flow show the 'three corridors of West-East Power Transmission (WEPT)' pattern, in which power is transmitted from some main thermal power-dominated provinces in central and north-west China to the eastern coastal provinces. Further, it resulted in the carbon leakage from electricity consumption of the coastal provinces and carbon overload of the hinterland provinces. Despite natural resource endowment, the location of the provinces in the national power grid, the national low-carbon energy transition strategy, as well as advances in long-distance ultra-high-voltage power transmission technologies were the main factors to the formation and evolution of Chinese electricity green transition. [ABSTRACT FROM AUTHOR]

This empirical study investigates the dynamic interconnection between fossil fuel consumption, alternative energy consumption, economic growth and carbon emissions in China over the 1981 to 2020 time period within a multivariate framework. The long-term relationships between the sequences are determined through the application of the Autoregressive Distributed Lag (ARDL) bounds test and augmented by the Johansen maximum likelihood procedure. The causal relationships between the variables are tested with the Granger causality technique based on the Vector Error Correction Model (VECM). Empirical results reveal the existence of a statistically significant negative relationship between alternative energy consumption and carbon emissions in the long-term equilibrium. Furthermore, the VECM results demonstrate that both carbon emissions and fossil fuel consumption have unidirectional effects on economic growth. Additionally, the study highlights a short-term unidirectional causal relationship from economic growth to alternative energy consumption. These findings suggest that a reduction in fossil fuel consumption in the short run may indirectly impede the development of alternative energy. The study proposes that China should expedite the development of alternative energy and control the expansion of fossil fuel consumption to attain its carbon reduction target without hindering economic growth. [ABSTRACT FROM AUTHOR]

The present study investigates electricity consumption, carbon dioxide (CO2) emission, and economic growth decoupling using data from 1971 to 2020 for the economy of China. The study uses decoupling analysis (DA) as the prime methodology for analysis. Furthermore, the findings put forward a significant contribution to an economic picture of the economy of China and a sizeable addition to related research and findings under the assigned issues discussed in the study. The study's main contribution is to decouple electricity consumption from the gross domestic product (GDP), which is rare in the existing literature in the context of China. Moreover, the study shows the decoupling of environment affects electricity consumption, and GDP growth. The DA model shows that electricity consumption is the main driving force enhancing economic growth. However, industrialization has increased greenhouse gases, global warming, and climate change due to production and consumption. China's economy uses coal for energy resources, which indicates that China produces a large proportion of electricity with coal, which causes high CO2 emissions. Finally, further analysis with the Granger causality test confirms the main findings. [ABSTRACT FROM AUTHOR]

China is expected to have a total installed photovoltaic capacity of 1300 GW in 2050, accounting for 39% of the national electricity consumption. However, air pollutants consisting of gases and particulates have attenuation effects on the solar radiation reaching the photovoltaic panels. This work purports to assess the influence of air pollutants on the photovoltaic power potential. We calculated the hourly point-of-array irradiance (POAI) in China for 2010–2020 with a spatial resolution of 0.1° × 0.1° using the PV_LIB model and assessed the effect of air pollutants on POAI. The results indicated that the annual average POAI in China for 2010–2020 ranged from 118 to 286 Wm−2. The Air Pollution Control Action Plan (APPCAP) has played a certain role in photovoltaic power potential, and POAI has increased in areas where surface concentrations of air pollutants have declined. Especially in North China, the surface concentrations of CO, NO2, PM10, PM2.5, and SO2 decreased throughout the APPCAP period with −0.446, −4.985, −35.610, −30.700, and −26.251 μgm−3, respectively, corresponding to an increase in POAI of up to 4.917 Wm−2. The surface concentrations of CO, NO2, PM10, PM2.5, and SO2 were negatively correlated with POAI, with correlation coefficients of −0.764, −0.854, −0.204, −0.110, and −0.664, respectively. Surface concentrations of air pollutants (CO, NO2, PM10, PM2.5, and SO2) and clear-sky POAI in 2018 showed a High-Low clustering in Northeast China and North China. This study demonstrates the role of China's air pollution control policy in enhancing photovoltaic power potential. [ABSTRACT FROM AUTHOR]

Since the concession period is one of the most crucial variables influencing the success of a photovoltaic (PV) power project under build-operate-transfer (BOT) mode, this paper presents a real option game model—which integrates the real option and cooperative game theory—to determine the optimal concession period of the BOT solar PV power project under policy incentives by considering the value of the option to defer. In the proposed model, an effective interval of concession period for the BOT project was identified by using a real option, and the equilibrium value of the concession period was obtained by applying classical optimization theory. To evaluate our model, we empirically determined the optimal concession period for the BOT solar PV power project in China, and a sensitivity analysis was conducted to demonstrate how relevant influential factors, which are presented in the model, affect the equilibrium value of the concession period and its effective interval. The results indicate that the current investment environment in China could successfully implement the BOT solar PV power project under policy incentives, including initial cost subsidy and the feed-in tariffs mechanism. While the high volatility of electricity demand, investment cost, and land-use rent fee can lead the failure of the BOT solar PV project implementation, promoting the technological development of PV power generation, maintaining the market stability, and increasing the policy incentives can help the BOT power project to be arranged with an optimal concession period. In addition, the feed-in tariffs mechanism is more helpful than the initial cost subsidy for winning the BOT contract. [ABSTRACT FROM AUTHOR]

There are many uncertainties and risks in residential electricity consumption associated with economic development. Knowledge of the relationship between residential electricity consumption and its key determinant--income--is important to the sustainable development of the electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of "prior restrictions". Robust tests of different segment specifications and regression methods are performed to ensure the validity of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. Government should also emphasize electricity conservation in the industrial sector rather than in residential sector. [ABSTRACT FROM AUTHOR]

Producing gridded electric power consumption (EPC) maps at a fine geographic scale is critical for rational deployment and effective utilization of electric power resources. Brightness of nighttime light (NTL) has been extensively adopted to evaluate the spatial patterns of EPC at multiple geographical scales. However, the blooming effect and saturation issue of NTL imagery limit its ability to accurately map EPC. Moreover, limited sectoral separation in applying NTL leads to the inaccurate spatial distribution of EPC, particularly in the case of industrial EPC, which is often a dominant portion of the total EPC in China. This study pioneers the separate estimation of spatial patterns of industrial and nonindustrial EPC over mainland China by jointly using points of interest (POIs) and multiple remotely sensed data in a random forests (RF) model. The POIs provided fine and detailed information about the different socioeconomic activities and played a significant role in determining industrial and nonindustrial EPC distribution. Based on the RF model, we produced industrial, non-industrial, and overall EPC maps at a 1 km resolution in mainland China for 2011. Compared against statistical data at the county level, our results showed a high accuracy (R2 = 0.958 for nonindustrial EPC estimation, 0.848 for industrial EPC estimation, and 0.913 for total EPC). This study indicated that the proposed RF-based method, integrating POIs and multiple remote sensing data, can markedly improve the accuracy for estimating EPC. This study also revealed the great potential of POIs in mapping the distribution of socioeconomic parameters. [ABSTRACT FROM AUTHOR]