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Rice production is the agricultural activity with the highest energy consumption and carbon emission intensity. Water and fertilizer management constitutes an important part of energy input for rice production and a key factor affecting greenhouse gas emissions from paddy fields. Water–fertilizer integration management (AIM) is an automated water and fertilizer management system for large-scale rice production, which can effectively save water and fertilizer resources. At present, the energy utilization and environmental impact of AIM in rice production are not clear. To clarify whether AIM is a water and fertilizer management measure that combines energy conservation and carbon emission reduction, a comparative study between the widely used farmers' enhanced water and fertilizer management (FEM) in China and AIM was conducted in this paper. Field experiments were conducted to evaluate the rice yield, carbon emission, energy utilization, and economic benefits of the two management methods. The results showed that AIM reduced water and fertilizer inputs, energy inputs, and economic costs by 12.18–28.57%, compared to FEM. The energy utilization efficiency, energy profitability, and energy productivity under AIM were improved by 11.30–12.61%. CH4 and N2O emissions and carbon footprint were reduced by 20.79%, 6.51%, and 16.39%, respectively. Compared with FEM, AIM can effectively improve the utilization efficiency of water and fertilizer resources and reduce carbon emissions. This study presents a mechanized water and fertilizer management approach suitable for large-scale rice production systems in China. By analyzing rice yield, resource utilization efficiency, and environmental benefits, AIM can serve as a crucial management strategy for enhancing productivity, economic returns, and environmental conservation within profitable rice production systems. In the future, further investigation into the impact of AIM on the microbial mechanisms underlying rice yield formation and greenhouse gas emissions is warranted. [ABSTRACT FROM AUTHOR]

The significant acceleration of China's urbanization process has greatly promoted economic development. At the same time, the massive construction of urban buildings has also caused many problems such as environmental pollution and increased energy consumption. Therefore, in architectural design, it is particularly important to pay attention to the sustainable development of the environment, handle the relationship between good people and nature under the guidance of the concept of green development, and focus on the recycling of resources. Focusing on resource utilization in architectural design and based on certain planning can better control the construction process of urban buildings, save energy consumption, reduce construction costs, and contribute to the green development of cities. The comprehensive evaluation of energy conservation and emission reduction of construction projects is a classical MAGDM problems. Recently, the Logarithmic TODIM (LogTODIM) method has been employed to cope with MAGDM issues. The single-valued neutrosophic sets (SVNSs) are used as a tool for characterizing uncertain information during the comprehensive evaluation of energy conservation and emission reduction of construction projects. In this paper, the single-valued neutrosophic number Logarithmic TODIM (SVNN-LogTODIM) method is built to solve the MAGDM under SVNSs. In the end, a numerical case study for comprehensive evaluation of energy conservation and emission reduction of construction projects is given to validate the proposed method. [ABSTRACT FROM AUTHOR]

The platform economy is growing fast in the world. Its expansion, exemplified by industry giants like Amazon, Airbnb, and Uber, is reshaping societal lifestyles and commercial businesses. However, its environmental impacts are uncertain. Currently, the relationship between electronic commerce (e‐commerce) activities and environmental pollution has attracted a lot of attention. This paper attempts to construct a comprehensive theoretical model to investigate the nexus between e‐commerce and regional environmental pollution, with a particular focus on the effects of air pollution and waste pollution. Fixed effect models and China's provincial data during the period from 2008 to 2018 are employed to examine the roles of energy efficiency and resource consumption in such a nexus. The findings suggest that the development of e‐commerce is linked to a reduction in regional air pollution through the channel of improving transportation energy efficiency. The results also show that e‐commerce is associated with an increase in regional waste pollution due to the mechanism of resource consumption in the express packaging industry. As a result, the relationship between e‐commerce and environmental pollution is quite complex at the regional level, transcending a binary distinction of solely positive or negative impacts. This paper contributes to a better understanding of the complex environmental impacts of e‐commerce and helps governments design more efficient and targeted policies to reduce regional pollution. [ABSTRACT FROM AUTHOR]

Due to their higher energy efficiency and better economic performance, distributed energy resource (DER) systems are expected to be one of the main energy supply forms in the future and have gained increasing attention in recent years. Thus, there is a need to boost our understanding of how to apply DER systems in different types of actual cases. This paper investigates a techno-economic analysis of a DER system applied in a real case in a hot-summer and cold-winter zone in China, where the climate is considered to be Cfa according to the Köppen–Geiger climate classification system. An urban underground transportation infrastructure system located in Changsha (China) was chosen to analyze the techno-economic performance of a natural-gas-fired DER system in comparison with a centralized energy system (CES). First, a scientific and reasonable application program of the natural-gas-fired DER system was developed by an overall load analysis (electric load, air-conditioner load, and domestic hot water load during the operating period). Based on this load analysis, this research combined the energy consumption and the actual operating situation and then compared and analyzed different types and capacities of equipment in this case. Moreover, a comprehensive analysis of the economic benefits was estimated by comparing the natural-gas-fired DER system with conventional CESs. Overall, the total annual cost of the DER system was reduced by 18.73%, and its additional investment can be paid back within about 2.2 years. A better economic benefit was achieved by applying the natural-gas-fired DER system in an actual case, which will help encourage the widespread application of DER systems. [ABSTRACT FROM AUTHOR]

The discrete grey modelling technique is a novel methodology of grey prediction models, which is effective to improve the effectiveness and applicability of grey models. In order to build a more general and effective univariate grey prediction model, the discrete grey modelling technique is utilised in this paper to build a Quadratic Polynomial Discrete Grey Model (QPDGM). The properties of the QPDGM have been discussed, which indicate that the new model can be regarded as an extension of the conventional discrete grey model and nonhomogeneous grey model, and it is also coincidence with three classes of exponential sequences. The QPDGM is finally applied to predict the energy consumption of China, including the electric power, crude oil and natural gas consumptions. The results have been compared to some commonly used univariate grey prediction models, which indicates the QPDGM is generally more accurate than other models. [ABSTRACT FROM AUTHOR]

In several rural areas of China, ecological public welfare work is an effective way to improve farmers' social participation. This job does not only increase farmers' income but also greatly improves their enthusiasm for ecological environment protection. Under the goal of carbon neutrality in peak carbon dioxide (CO2) emissions, it is necessary to explore the impact of ecological public welfare jobs on the usage of Clean Energy (CE) in rural areas. Based on the data of 1100 farmers from Tibet areas in China, this paper applied the Ordered Probit model to explore the impact of ecological public welfare jobs on farmers' use of CE. The results are as follows: (1) Holding ecological public welfare jobs can raise farmers' willingness to use CE; (2) Holding ecological public welfare jobs can also promote farmers' use of CE by enhancing their ecological environment cognition and influencing their social behavior; (3) The impact of ecological public welfare work on CE use has regional and income heterogeneities. Firstly, this effect is smaller in mixed pastoral-farming areas than in agricultural and pastoral areas. Secondly, this effect is more obvious in low-income groups. Our study provided several policies aimed at improving rural and environmental development. [ABSTRACT FROM AUTHOR]

High temperature impacts the performance of marathon athletes, and hypothermia harms athletes. Twenty-one runners died, and eight were injured in the China Baiyin marathon on May 22, 2021. It's a typical human life test. The energy equations are combined with the maximum energy supply of Chinese male athletes to study this accident. We analyze the human body's route slope, travel speed, and heat dissipation under low temperatures in this marathon. The study shows that the large slope and long-distance of CP2 to CP3 section and the low temperature during the competition are the main reasons for the accident. The method of quantifying the slope and temperature and calculating the percentage of athletes' physical consumption proposed in this paper can evaluate the route design of field marathons. We suggest that the physical energy consumption ratio of 90%, i.e. 315 cal/min/kg, should be taken as the maximum energy supply for Chinese male marathon runners. Dangerous risk zones for wind speed and temperature on dangerous path sections are also formulated for athletes to make their assessments. This paper's theories and methods can effectively help design the marathon route and determine the race time. [ABSTRACT FROM AUTHOR]

Energy and environmental policies are important methods for the government to restrain carbon emissions growth. Identifying the potential dynamic trends of China's carbon emissions under different scenarios has important reference significance for the government's policy implementation. This paper firstly predicted China's carbon emissions from 2017 to 2040 based on three energy transition scenarios at the industrial level. Then, Logarithmic Mean Divisia Index decomposition model was applied to evaluate the driving forces of emissions changes during 1997–2040. Finally, the Spatial–Temporal Logarithmic Mean Divisia Index model was used to explore the emissions reduction potential and the potential reduction path at provincial level. The results showed that (1) as the reduction in energy intensity cannot offset the growth of industrial scale, the carbon emissions of all industries have shown an increasing trend from 1997 to 2017; (2) In the current policies scenario, China's carbon emissions cannot reach the peak before 2040. And only in the sustainable development scenario, the carbon emissions of the three industries will all reach the peaks before 2030. And the development of non-fossil energy will reduce carbon emissions by more than 30%; (3) Hebei, Shanxi, Inner Mongolia, Ningxia, and Heilongjiang are key provinces and improving energy efficiency of the secondary industry is a potential way to promote carbon emissions reduction. The framework and main content of this paper. [ABSTRACT FROM AUTHOR]

With China's economy entering the stage of high-quality development, manufacturing energy carbon emission efficiency has become the focus of academic attention. It is of great significance to study the convergence of manufacturing energy carbon emission efficiency for realizing high-quality development of manufacturing in China. Based on the panel data of China's manufacturing sub-sectors, this paper measures and analyzes the evolution trend of manufacturing energy carbon emission and its efficiency. On this basis, this paper uses the coefficient of variation and convergence model to test the convergence of manufacturing energy carbon emission efficiency. The results show that China's manufacturing energy carbon emissions and its efficiency demonstrate an increasing trend. Coal was the main source of manufacturing energy carbon emissions. The manufacturing energy carbon emission efficiency does not have σ convergence, but has β convergence, and its convergence has industry heterogeneity. The manufacturing energy carbon emission efficiency exits scale effect and technology effect, but not the effect of opening to the outside world and institutional effect, and its effect exists industry heterogeneity. By reducing carbon emissions, adopting differentiated policies, adjusting the industry scale, and enhancing the industry technology intensity, China's manufacturing can improve the energy carbon emission efficiency and promote high-quality economic development. [ABSTRACT FROM AUTHOR]

To assess the characteristics of household carbon emissions per capita (HCPC), this paper divided China's provinces into 4 groups based on the decoupling relationship between household consumption and related emissions. This classification helped to analyze the correlation and reflected the decoupling status between carbon emissions and household consumption and explored the effect of consumption growth on carbon emissions. Then, according to logarithmic mean divisia index (LMDI) model, HCPC in China's provinces was decomposed into four drivers including carbon coefficient, energy structure, energy consumption, and population structure effect. Through multi-regional (M-R) analysis, temporal evolution and spatial differences of these four drivers in both national and provincial level were studied. This comparison method introduced temporal and spatial decomposition results into the same framework, which may provide a new perspective for analyzing carbon emission trends. The results showed that (a) the HCPC in all 30 provinces increased significantly especially in Inner Mongolia, Tianjin, Xinjiang, Heilongjiang, and Beijing. Energy consumption effect was the leading factor promoting HCPC growth. Energy structure and population structure also promoted HCPC growth slightly, and carbon coefficient was the effect which had inhibitory effect on HCPC growth at regional level. (b) Spatial differences of HCPC between regions narrowed during this period. This is mainly due to the rapid growth of HCPC in region IV. Energy consumption effect was the dominant factor for the spatial differences. Based on the results, this paper proposed to adopt more effective measures to improve energy efficiency, develop clean energy, and optimize energy structure, especially in the provinces with faster growth in carbon emissions. [ABSTRACT FROM AUTHOR]

This study provides a viable path to save energy by means of e-commerce development. Taking the national e-commerce demonstration cities (NEDC) pilots policy implemented in China as a quasi-natural experiment, based on the city panel data from 2006 to 2019, this study applies the multi-period difference-in-difference (DID) method to evaluate the effect of NEDC on energy saving in pilot cities. The empirical results suggest that the NEDC policy obviously contributes to energy conservation. The treated cities reduced energy consumption by 14.2% as a result of the implementation of NEDC, relative to the untreated cities. The conclusions remain valid after conducting robustness tests such as placebo test, instrumental variables regression, propensity score matching-difference-in-difference (PSM-DID), and synthetic difference-in-difference (SDID). The NEDC achieves energy-saving effects through technological innovation, industrial restructuring, and economic agglomeration. Furthermore, the heterogeneity analysis indicates that, in cities with high levels of human capital, well-developed information infrastructure, non-resource-based cities, and favorable business environments, the impact of NEDC on energy saving is more significant. Analysis of spatial effects shows that the implementation of NEDC has negative externalities, increasing energy consumption in the surrounding area. In the context of the digital economy, this paper presents new insights on the relationship between e-commerce and energy consumption and provides policy direction for countries looking for energy-saving solutions. [ABSTRACT FROM AUTHOR]

Currently, coal is still the main energy source for China's economic development, and the environmental issues caused by coal consumption have aroused widespread concern. Predicting and analyzing coal consumption will help to formulate a higher level of energy consumption planning and economic development strategy. The main purpose of this study is to develop a new fractal time-series prediction (FTSP) model to predict coal consumption, providing reference for formulating effective energy consumption planning and economic development strategies. Hurst index is an important indicator of whether time-series data have long memory and fractal characteristics. This paper uses the rescaled range (R/S) method to calculate Hurst index of the coal consumption per-10000-Yuan-GDP (CC/GDP). The Hurst index is 0.8025, exceeding 0.5, indicating that the time-series data CC/GDP has a long memory and fractal characteristic. Based on this, this paper uses the FTSP model to predict China's coal consumption (CC/GDP). The prediction results show that the FTSP model accurately predicted China's coal consumption with a relative error rate (RER) of less than 5% and a mean absolute percentage error (MAPE) of 6.62%. The model predicts a decrease in China's coal consumption from 0.25 tons of standard coal in 2021 to 0.11 tons of standard coal in 2030, a decrease of 44%. To sum up, the FTSP model provides a new and accurate way to predict coal consumption, and the results suggest that China's coal consumption is sustainable and will decrease significantly in the coming years. This study has important implications for energy consumption planning and economic development strategies. [ABSTRACT FROM AUTHOR]

Copyright of China Population Resources & Environment is the property of Shandong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Low-carbon transition has gradually become the focus of research on environmental issues. This paper takes China's eight major economic regions as the entry point. First, carbon emissions are measured according to United Nations' baseline methodologies. Second, the stochastic nonparametric data envelope analysis (StoNED) model is used to measure energy efficiency to improve the accuracy of the measurement. Finally, considering the temporal and spatial nonstationarity of carbon emission data, this paper constructs geographically and temporally weighted regression-stochastic impacts by regression on population, affluence, and technology (GTWR-STIRPAT) model, which can accurately analyze the impact of each driving factor of carbon emissions. This paper also explores efficient emission reduction paths in conjunction with the forcing mechanism. According to the study, China's carbon emissions show a decreasing trend from coastal areas to inland areas. In addition, there are significant problems with carbon emissions in China: some regions focus on improving energy efficiency but neglect increasing energy consumption; some regions focus on industrial development but neglect long-term emission reductions. Among the driving factors, energy efficiency, foreign trade, environmental regulations, and industrial structure have the effects of spatiotemporal heterogeneity, spatial heterogeneity, and time lag on carbon emissions, respectively. [ABSTRACT FROM AUTHOR]

Under the new economic background, the new normal characteristics of China's power development are also obvious, highlighting the slowdown in the growth of electricity consumption in the whole society, the overall downturn in power consumption in high energy consumption industry, the continuous optimization of power structure, and the more obvious regional differences. In view of these problems, this paper analyzes the economic and stability of the power grid and the constraints of the power grid stability and the transmission capacity of the transmission section, and constructs the economic consideration with the minimum purchase cost and the minimum discharge amount of the pollutant gas as the objective function. And the particle swarm optimization algorithm is used to solve the problem. In this paper, an example is given to calculate and analyze the actual grid, and the validity and feasibility of the model are verified. [ABSTRACT FROM AUTHOR]

To protect energy resources and alleviate environmental pollution, many countries attach great importance to the transformation of traditional industries into clean energy industries. In this paper, fuel vehicles (FVs), hybrid vehicles (HVs), and electric vehicles (EVs) are included in the research. Then, based on the aging chain and coflow theory of SDs, we construct a dynamic matching model of the auto industry upgrade process and its energy consumption attributes. The simulation results of China's auto industry show that (1) the upgrading of the auto industry is an evolutionary process from high energy consumption and high pollution to low energy consumption and no pollution and the transition from FVs and HVs to EVs will undergo two adjustments; (2) simply reducing energy supply does not have the expected impact on vehicle size and vehicle energy consumption intensity and only by adjusting the energy supply and upgrade ratios together, energy utilization efficiency can be improved; (3) market screening time has an impact on auto industry upgrade speed by affecting vehicle market share and dwell time; (4) China's auto industry upgrade process should adhere to "problem-oriented" and strengthen consumer guidance, technology innovation, and infrastructure construction. The conclusions can provide references for industrial policy adjustment and industrial structure optimization. [ABSTRACT FROM AUTHOR]

Based on the perspective of market segmentation, this paper empirically tests the impact mechanism of market segmentation on energy efficiency by using China's provincial panel data from 2005 to 2016. The results show that market segmentation has a significant inhibitory effect on energy efficiency by distorting technological progress, scale efficiency, and allocation efficiency. The conclusion of this paper not only provides an important theoretical basis for promoting the reform of China's economic system, but also brings some enlightenment for other developing countries to choose ways to improve energy efficiency. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to determine the spatial spillover effects of renewable energy on carbon emissions in China's less-developed areas. However, few studies have considered this issue from the perspective of less-developed areas. Based on panel data of 21 provinces in China from 2000 to 2017, this paper analyzes the spatial spillover effects of renewable energy on carbon emissions using Moran's I and Spatial Durbin Model (SDM). The results suggest that, first, Moran's I ranges from 0.378 to 0.519, Moran scatter plot presents that provinces are located in the high–high (HH) and low-low (LL) quadrants, indicating provincial carbon emissions in the study area have a significant spatial correlation and agglomeration. Second, under the three matrices, the direct effect coefficients of renewable energy are -0.2522, -0.2639 and -0.2601, this shows that renewable energy is beneficial to local carbon emissions reduction. In contrast, the indirect effect coefficients of renewable energy are 0.0605, 0.1012 and 0.1125, which means higher renewable energy consumption in a single area is conducive to the improvement of carbon emissions to neighbouring areas. Third, urbanization, industrialization, physical capital and other variables have different impacts on local and nearby carbon emissions. This study provides empirical evidence to achieve carbon emission reduction targets by government policymakers. [ABSTRACT FROM AUTHOR]

The sustainable development of China's economy is bottlenecked by resource shortage and environmental pollution. As the leading resource consumer and pollutant source, the industrial sector needs to improve its energy efficiency. This paper establishes a super epsilon-based measure (Super-EBM) model with bad outputs like environmental cost and evaluates the industrial green total-factor energy efficiencies (IGTFEEs) of 30 provinces in China during 2000–2017. Unlike previous research, the main contribution of this paper is to choose four environmental pollutants as bad outputs (industrial carbon dioxide, industrial sulfur dioxide, industrial chemical oxygen demand, industrial solid waste). By contrast, the previous studies mostly only take one environmental pollutant as bad output, i.e., the bad outputs are not fully measured. Then, the spatiotemporal dynamics and spatial correlations of the IGTFEEs were analyzed, and the influencing factors of IGTFEE were examined empirically with a spatial econometric model. Finally, this paper adopts generalized method of moments (GMM) to solve the endogenous problem, trying to assure the robustness of estimation results. The results show significant provincial differences in IGTFEE. Most eastern coastal provinces achieved satisfactory IGTFEEs, while most inland provinces had undesirable IGTFEEs. Eastern region achieved the highest IGTFEE, followed by central region; western region had the lowest IGTFEE. The IGTFEE improved over time in some provinces while worsened greatly in some provinces. The IGTFEE in most provinces need to be further improved. Global Moran's I values indicate that the provincial IGTFEEs were clustered in space, rather than randomly distributed. Local indication of spatial association (LISA) map reflects significant local spatial clustering of provincial IGTFEEs. In addition, IGTFEE is significantly promoted by industrial structure, technological innovation, human capital, opening-up, and energy structure yet significantly suppressed by ownership structure and environmental regulation. Considering the endogeneity, GMM results show that the estimation results of the model were robust. Specific policy recommendations include vigorously developing high-tech industries, deepening state-owned enterprises reform, diverting more funds to research and development, cultivating versatile talents, introducing environmentally-friendly foreign capital, accelerating the implementation of clean energy development strategy, and widening the fund channels of pollution control investment. [ABSTRACT FROM AUTHOR]

A significant corridor under the "Belt and Road Initiative" is announced by the government of China for numerous transport and energy projects. China focuses greatly on the sustainability of these projects, in accordance with Green Investment Principles. For this, environmental, social, governance, and technological considerations are taken into account. This paper offers a novel assessment of the impact of green sustainability under BRI and the current emission scenario in Pakistan. The question that needs to be addressed here is whether the projects under Belt and Road Initiative are as environment friendly as they claim. In the first phase of this study, we review theories establishing evidence based on existing literature to determine the influence of Belt and Road on economic development. In the next phase, we use Environment Kuznets Curve and apply estimates from Johansson's Cointegration Test to quantify the present environmental scenario of Pakistan and probable environmental risk portrayed by the China-Pakistan Economic Corridor (CPEC). This paper helps in determining the relationship between carbon emission and three major variables including energy consumption, economic growth, and foreign direct investment (FDI). Cointegration analysis is applied on time series data comprising of a 40 years' period from 1979 to 2019. The paper finds that (i) the belt and road corridors could substantially improve trade, foreign investment, and living conditions for citizens in participating countries; (ii) moreover, this study confirms the presence of one cointegrated equation suggesting the presence of a long term relationship between the CO2 and independent variables and a short run association running from EnC to CO2. The study also proposes a Green BRI Model which formulates a body that devises environmental performance standards, reviews procedures, and ensures that they get implemented on all CPEC projects. The Belt and Road Initiative increases emissions among participating countries. China and corridor economies need to adopt strict policy reforms that increase transparency, expand trade, improve debt sustainability, and mitigate environmental, social, and corruption risks. [ABSTRACT FROM AUTHOR]

Under the policy background of "joint prevention and control" of global greenhouse gas emission reduction and regional air pollutants, the power industry, as an important target industry of energy conservation and emission reduction policies, has become an effective choice to release dual pressures. In this paper, the "bottom-up" emission factor method was used to measure the emission of CO2 and NOX from 2011 to 2019. Then, the contributions of six factors to NOX emission reduction in China's power industry were identified using the Kaya identity and logarithmic mean divisia index (LMDI) decomposition methods. The research results show that (1) there is a significant synergistic emission reduction effect between CO2 emission reduction and NOX emission reduction; (2) the factor that inhibits the growth of NOX emissions reduction in the power industry is economic development factor; and (3) the main factors that promote the reduction of NOX emission from the power industry are synergy effect, energy intensity, power generation intensity, and power production structure factors. Several suggestions are proposed, which are the power industry should adjust its structure, improve energy intensity, focus on applying low-nitrogen combustion technology, and improve the air pollutant emission information disclosure system to reduce NOX emissions. [ABSTRACT FROM AUTHOR]

The Clean Heating Policy aims to solve the problems of excessive energy consumption and severe air pollution caused by central heating in northern China. Whether this policy can effectively reduce carbon emissions remains unexplored. Using panel data representing 65 cities in northern China from 2010 to 2019, this paper constructs a dynamic spatial DID model to empirically study the carbon reduction effect of the Clean Heating Policy and its influence channels. The results are summarized as follows. First, the Clean Heating Policy can significantly reduce carbon emissions, and this conclusion holds after multiple robustness tests. The policy has a lag effect, but its spatial spillover effect and long-term effect are not significant. Second, the carbon reduction effect of the Clean Heating Policy is mainly achieved by optimizing the energy structure and improving the thermal efficiency of heat consumer terminals. Third, the carbon reduction effect varies by city and emission field. It is significant only in low-subsidy cities, high-carbon cities, and household fields. Fourth, there is a synergistic reduction relationship between the Clean Heating Policy and the low-carbon city policy. Based on the results of this paper, we propose policy implications, such as promoting policies in multiple ways and improving subsidy efficiency, and provide a reference for other countries. [ABSTRACT FROM AUTHOR]

Our paper investigates the association of renewable energy, financial development, urbanization, and growth in China along with technology. The newly developed econometric techniques (asymmetries) have been applied to explore short and long-run nonlinear relation among variables by utilizing annual data for the period of 1965–2021. In nonlinear cointegration, findings highlight the importance of renewable energy consumption for growth and development. Urbanization also confirms a positive association with growth and development where positive shocks were more dominant to support the growth process. The stock of technology brings a positive impact on growth and development whereas financial development results reveal to consider policy carefully. Asymmetric causality supports a uni-directional causal association between renewable energy, financial development, and growth. Policy recommendations are also highlighted according to the results. [ABSTRACT FROM AUTHOR]

Stimulating the shift to low-carbon energy and decreasing energy intensity are crucial strategies for green growth. Reducing energy intensity is an important measure to achieve the goal of "double carbon" and building a beautiful new China. Based on the provincial panel data of China from 2011 to 2020, this paper empirically tests the relationship among digital financial inclusion, industrial structure distortion, and energy intensity by using the spatial Durbin model and the intermediary effect method. The results show that the development of digital financial inclusion can promote the decline of energy intensity, and industrial structure distortion has a partial intermediary effect in the relationship between digital financial inclusion and energy intensity. Because of this, it is important to speed up the development of digital financial inclusion, increase the innovation of digital financial inclusion products and services, strengthen the supervision of digital financial inclusion, and reduce the distortion of the industrial structure so that digital finance can play its full role in reducing energy intensity. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the scale of construction projects in China is gradually expanding. In terms of construction technology, continuous innovation is being made to meet the needs of sustainable development strategies, and green energy conservation technologies have emerged. It has a brand new design concept, and advocates for maximum energy conservation and environmental protection, fundamentally promoting the rational use of resources and space, and preventing the expansion of air and land pollution. First of all, a brief explanation is given on the current development status of green energy conservation technology in this paper. Secondly, through the introduction and analysis of energy conservation technologies and related building cases, the in-depth research on energy conservation measures of buildings is conducted. Finally, the prospects of green energy conservation technologies are proposed based on the current development status in China. This paper has certain reference value for its related engineering and theoretical research. [ABSTRACT FROM AUTHOR]

In recent years, with the gradual implementation of the central government's policy of expanding domestic demand, the living standards of urban residents have been greatly improved. With the increasing level of urban living consumption, residents' living consumption situation has attracted more attention and attention. In order to further increase the consumption income of households, it is necessary to conduct corresponding analysis on households in various regions. Based on SPSS statistical simulation software, this paper uses factor analysis and clustering methods often used in modern multivariate economic statistical analysis to establish multivariate statistical models and calculate and establish various analysis models suitable for my country's household consumption expenditure structure (2020) and analyze the parameters, accordingly. From the perspectives of politics, economy, geography, etc., this paper reveals the changes in energy consumption in China's family and social life and its possible implied related information, laws, directions, and the overall development level of society. Through empirical analysis and research, this paper can finally conclude that the current consumption expenditure structure of Chinese households is affected by a combination of factors such as national policies, economic development and technical conditions, and geographical environment characteristics, and the effect is different in different cities. [ABSTRACT FROM AUTHOR]

The massive use of energy has caused a rapid increase in global carbon dioxide emissions, resulting in a series of environmental problems such as climate warming. Investment in the energy industry can guide funds into green and clean production, reduce carbon emissions in the energy industry, and promote the green development of the energy industry. This paper considers the energy, the environment, the economy, and other factors and focuses on energy consumption and investment structure. Taking 30 provinces in China as research samples, a dynamic spatial Durbin model is established. The results show that the first-order term of carbon emissions has a driving force of 0.5068% for current carbon emissions at a significance level of 1% and that the increase in current carbon emissions will lead to a continued increase in carbon emissions in the next period. The increase in the carbon emissions of neighbouring provinces will increase their carbon emissions through the spatial spillover effect. Whether in the short term or long term, the increase in energy investment and the optimization of the energy investment structure can reduce carbon emissions. The above conclusions can provide a reference for the formulation of government environmental policies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the influencing factors of the ecological pressure of the energy carbon footprint (EPECF) of China's whole industry from 2000 to 2018. First, the EPECF of 48 sub industries is calculated, then divides 48 sub-industries into high-, medium-, and low-pressure industries, and uses the logarithmic mean Divisia index (LMDI) method to analyze and summarize the main driving forces of China's industrial EPECF changes. Finally, policy suggestions for the future industrial decompression are put forward. The main results are as follows: (1) Economic development is the most important factor to promote the growth of EPECF of the three major industries. (2) At present, the population pressure factors of forest and grassland have little effect, and the effect of returning farmland to forest and grassland has not been truly played. (3) The adjustment of industrial structure has gradually become a key factor in reducing EPECF of the three industries. (4) The gradual stability of energy intensity has a certain inhibitory effect on the increase of EPECF in high-pressure industry. (5) The adjustment of energy structure in low-pressure industry has gradually worked. Therefore, the government should establish an economic sustainable development system, vigorously develop clean energy, and realize the green transformation of various industries. This provides an empirical example for other countries in the world to reduce the EPECF. [ABSTRACT FROM AUTHOR]

As one of the industries with high energy-extensive consumption in China, the carbon emissions (CO2-E) generated by heavy industry cannot be ignored. However, the related literatures that take heavy industry energy-related CO2-E as the research object in the past mainly focus on the index decomposition model (IDM), econometric analysis, and other aspects. At present, few literatures use the structural decomposition model (SDM) to analyze the CO2-E and influencing factors of the industry from the perspective of input–output (I-O) analysis. This perspective and model can unify the supply side and demand side energy-related CO2-E of the heavy industry, which is conducive to a more comprehensive analysis of the heavy industry energy-related CO2-E and further various direct and indirect influencing factors. Based on this, this paper employs the energy consumption method (ECM), the I-O analysis method, and SDM to examine the energy-related CO2-E and influencing factors of China's heavy industry. The findings show that while the growth tendency of China's heavy industry energy-related CO2-E has been effectively controlled within the sample interval. Simultaneously, the supply side's optimization of the energy consumption structure (ECS), the upgrading of energy utilization technologies, and the generalized technological progress rate (GTPR) reflecting the input structure effect (ISE) have an inhibitory effect on the heavy industry's energy-related CO2-E, while the final demand effect (FDE) has an increasing effect on the energy-related CO2-E in the heavy industry, indicating that the products produced by the heavy industry are still characterized by high carbonation. This paper not only further enriches the existing research literature on energy-related CO2-E from the heavy industry in theory, but also provides guidance for efficient control of excessive growth of energy-related CO2-E from the perspective of input and output for the heavy industry in practice. [ABSTRACT FROM AUTHOR]

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Expanding the supplies of new energy can not only reduce CO2 emissions, but also alleviate energy shortage. This paper applies the quantile regression to investigate the new energy industry in China. The results show that economic growth exerts the greatest effect on the new energy industry in the lower 10th quantile province. This is because these provinces have the developed economies, demand for a higher ecological environment and new energy resources. Foreign energy dependence has a minimal impact on the new energy industry in the 25th-50th quantile province, due to their minimal oil importation. The contribution of technological progress to the upper 90th quantile province is the lowest, because their R&D capabilities are the weakest. The impact of energy consumption structure decreases in steps from the lower 10th quantile provinces to the upper 90th quantile provinces. The agricultural sector promotes the new energy industry in most provinces. [ABSTRACT FROM AUTHOR]

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China's automotive industry has been dedicated to a series of carbon-reduction efforts and has strived for comprehensive green and low-carbon transformation in order to achieve carbon peaking and carbon neutrality goals. On the basis of automobiles' whole life-cycle (WLC) carbon emission accounting, this paper calculates life-cycle carbon emissions per vehicle, proposes green and low-carbon development path for China's automotive industry, quantifies and analyzes implicated carbon-reduction potential, and puts forth suggestions for high-quality green and low-carbon development of China's automotive industry. The first is to establish a sound standard and data management system; the second is to promote research, development and application of low-carbon materials and low-carbon technologies; the third is to accelerate the pace of fostering a new consumption model in the automotive industry. The research results can further support national policy-making regarding carbon emissions, promote corporations' research, development and application of low-carbon technologies, encourage green and low-carbon consumption, and lead the automotive industry to achieve WLC neutrality. [ABSTRACT FROM AUTHOR]

Energy is one of the critical factors for economic growth, and carbon emissions are by-products of energy use that degrade environmental quality. This research thus establishes an index of eco-output elasticity of energy consumption to find an eco-friendly production stage in which not only output increases, but also energy consumption degrades. Our observation subject is China, which employs its own energy intensity index to monitor the nexus of economy, energy, and environment. While its energy intensity index is unable to point out the eco-friendly production stage, the index of eco-output elasticity of energy consumption herein does. This paper offers three main empirical findings on China: (i) it presents an eco-friendly production stage from 2012 to 2013; (ii) it has better improvement on energy savings than CO2 reduction; and (iii) in general, its status quo is still high energy consumption, but this phenomenon is improving. [ABSTRACT FROM AUTHOR]

As a financial innovation model to promote sustainable economic development, carbon finance promotes green and inclusive economic development by reshaping the financial system. As a big energy consumer, China has been committed to the green transformation of energy structure, and carbon finance has been regarded as an important tool to support carbon emission reduction. This paper divides carbon finance from three levels, financial macro, environmental governance, and technological development, and selects 17 indexes to measure the development level of carbon finance in China. Time series regression model and the entropy weight method (EWM) are used to analyze the impact of carbon finance on China's energy consumption structure. The empirical results show that carbon finance plays a positive role in promoting China's energy consumption structure, and developing carbon finance is conducive to the use of clean energy and the optimization of energy consumption structure in China; carbon finance can further optimize China's energy consumption structure by investing capital, resources, and technology in industries related to promoting clean energy consumption. Therefore, China should continue to develop carbon finance and promote the establishment of a unified national carbon trading market; at the same time, China should use the CCER trading mechanism to promote the development of renewable energy and enhance the endogenous driving force of energy transformation by promoting technological progress. [ABSTRACT FROM AUTHOR]

Climate change has a profound impact on human survival and development. Climate change is an energy and economic issue, which should be driven by technology. Total factor energy efficiency (TFEE) improvement is undoubtedly a breakthrough in solving energy problems. In this paper, the fixed effect model, impulse response function, and threshold regression model are used to test the complex relationship between export sophistication and TFEE. The results reveal that export sophistication improvement leads to higher TFEE. The impulse response results verify the existence of a phased and positive correlation between export sophistication and TFEE. Finally, the relationship between export sophistication and TFEE significantly varied across different threshold levels of regional corruption score, economic development, and openness. This article led the foundation for supporting China's export sophistication promotion strategy and also provides a reference for energy efficiency improvement and energy crisis response in the post-pandemic era. [ABSTRACT FROM AUTHOR]

Renewable energy consumption has a strong impetus in promoting energy conservation and emission reduction, which is a new path leading to clean and low-carbon development. Based on that, this paper uses the data of carbon productivity, renewable energy power consumption level, technological progress, national economic development level, population, energy efficiency, industrial structure rationality, and other data in 30 provinces in China from 2011 to 2020, based on the STIRPAT model, conducts an empirical analysis on the impact of renewable energy power consumption on carbon productivity in Chinese provinces while considering both the spatial horizontal dimension and the temporal vertical dimension. The empirical results show that (1) Chinese carbon productivity presents an obvious spatial spillover effect and presents the spatial positive correlation distribution characteristics of "high-high" type agglomeration and "low-low" type agglomeration. (2) The utilization of renewable energy plays a positive role in promoting the development of low-carbon economy. The perspective of the horizontal spatial dimension shows a positive spatial spillover effect. The perspective of the longitudinal time dimension shows a marginal increase in the overall improvement of the environment. (3) Among the seven regions in China, the consumption of renewable energy in North China, East China, and Central China brings a dominant effect on carbon productivity. (4) About 29% of the positive effect of renewable energy consumption on carbon productivity is indirectly realized by technological progress. Finally, the article puts forward targeted policy suggestions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to strengthen research concerning the impact of climate change on China's water resources, in order to avoid extreme weather events such as regional rainstorms and floods affecting the stable supply of energy and electricity. The goal is to reorganize energy industry allocation to make China's energy the most efficient, which would thus help achieve sustainable development goals. The biggest contribution and of this paper is not only to enlarge the scale and take into account the sustainable aspects, but also to consider climate change factors that have been ignored in the past as exogenous variables. For the first time, we take stock of the optimal allocation of new and tradition energy sources and carbon dioxide. This study uses the Dynamic ZSG-DDF model with exogenous variables and undesirable outputs to find a way to redistribute carbon dioxide, new energy, and tradition energy from different provinces on the premise of maximizing efficiency, thus achieving the goal of reducing carbon and saving the earth. The research results show that the energy efficiency of tradition energy is higher than that of new energy in various provinces. At the same time, the China government must comprehensively review the quota targets of new energy, tradition energy, and carbon dioxide (CO2) in each province as quickly as possible. Currently, the quotas of various provinces are seriously misplaced, and 26 provinces in total have great room for improvement. [ABSTRACT FROM AUTHOR]

The level of energy green consumption is an essential factor influencing China's economy's high-quality development. In this paper, we select relevant panel data from 2010 to 2019 for 28 cities in Beijing-Tianjin-Hebei and surrounding areas, and use the DPSIR model and spatial econometric model to analyze the spatial divergence characteristics and driving factors of the energy green consumption index based on the construction of the energy green consumption level evaluation index system and index measurement. The results of this research are as follows: (1) The energy green consumption index has a distribution pattern of "high in the east and low in the west, high in the north, and low in the south," and the driving force (D) and response force (I) are the key factors influencing the spatial variation of the energy green consumption index. (2) The measurement results show that GDP per capita and built-up green coverage have a considerable positive effect on the energy green consumption index, whereas non-domestic energy structure has a significant inhibitory effect. (3) In terms of spatial spillover effects, greening coverage of built-up areas, industrial structure, and domestic energy structure, all have positive effects on neighboring regions' energy green consumption indexes, with positive spatial spillover effects, whereas GDP per capita, urbanization level, and non-domestic energy structure, all have negative spatial spillover effects. In light of the issues at hand, this paper suggests making good efforts to create and put into practice laws and policy norms, cultivate green energy-saving markets, vigorously promote the use and promotion of energy-saving technologies, and lower energy consumption in secondary industry and domestic consumption. The study's findings can help advance the modernization of the region's industrial structure and the growth of a green economy by providing the local government with a reference point for timely policy adjustments. [ABSTRACT FROM AUTHOR]

The dual-credit policy, implemented by the Chinese government on April 1, 2018, aims at boosting the production of new energy vehicles and further reducing fuel consumption of internal combustion engine vehicles. However, whether the policy is effective has caused controversy among many scholars in China and one of the hot spots of debate is about the effectiveness of the pricing method. In this paper, the government pricing model of dual-credit policy and the market pricing model are compared by the optimization theory. The factors include the transaction price of credits, the quantity of vehicles, the monopoly manufacturer's profits, and social welfare. Our results show that the dual-credit policy benefits energy saving and emission reduction in the transport sector. In addition, it is suggested that the government should formulate the transaction price of credits and subsidy the automobile enterprise in the early stage of the policy while adopting free market pricing in the late stage of the policy. [ABSTRACT FROM AUTHOR]

In recent years, the rapid rise of carbon dioxide concentration makes global warming increasingly serious, which leads to sea-level rise, coastal lowland inundation, glacier melting, extreme weather, species extinction and other environmental problems. China, as the largest carbon dioxide emitter, is facing climate change. In 2020, the Chinese government promised at the UN General Assembly to achieve the peak of carbon by 2030, and also put forward the goal of achieving carbon neutrality by 2060. To achieve the carbon peak and carbon in target, China needs to reduce the use of fossil energy and replace the use of fossil energy with renewable energy with less carbon dioxide emissions. Tidal energy, as a low-carbon renewable energy, is an important alternative to fossil energy. But can the tide be developed at the expense of the environment? In this paper, the impact of the development of tidal energy on the environment is analyzed and elaborated. According to the environmental impact, the solutions of developing environment-friendly tidal energy, wave energy and temperature difference energy are given. Based on this, policy suggestions are put forward to the Chinese government. The research results of this paper will provide reference for developing countries to develop and utilize tidal energy for power generation. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between "replacing business tax with value-added tax" (RBTVT) and the total factor energy efficiency (TFEE) of the logistics industry using regression discontinuity (RD) method, and the research shows that the TFEE of the logistics industry in China has presented a benign growth trend. RBTVT has significantly promoted the development of TFEE in the logistics industry, and its influence has mainly stemmed from the improvement of green technology progress. Moreover, the promotion mechanism of policy in the eastern region reacts more sharp than that in the central and western regions does. So this paper claims that the government should built a tax incentive mechanism of green production and energy conservation. It is necessary to use RBTVT to promote the high-quality development of the logistics industry and create an environment which coexist low-carbon environmental protection and economic benefits. [ABSTRACT FROM AUTHOR]

With the acceleration of China's energy transformation process and the rapid increase of renewable energy market demand, the photovoltaic (PV) industry has created more jobs and effectively alleviated the employment pressure of the labor market under the normalization of the epidemic situation. First, to accurately predict China's solar PV installed capacity, this paper proposes a multi-factor installed capacity prediction model based on bidirectional long short-term memory-grey relation analysis. The results show that, the MAPE value of the GRA-LSTM combined model established in this paper is 5.995, compared with the prediction results of other models, the prediction accuracy of the GRA-BiLSTM model is higher. Second, the BiLSTM model is used to forecast China's installed solar PV capacity from 2020 to 2035. The forecast results show that China's newly installed solar PV capacity will continue to grow and reach 2833GW in 2035. Third, the employment number in China's solar PV industry during 2020–2035 is predicted by the employment factors (EF) method. The results show that the energy transition in China during 2020–2035 will have a positive impact on the future stability and growth of the labor market in the solar PV industry. Overall, an accurate forecast of solar PV installed capacity can provide effective decision support for planning electric power development strategy and formulating employment policy of solar PV industry. [ABSTRACT FROM AUTHOR]

In order to evaluate the effect of market-oriented environmental management measures on regional carbon emission intensity in the pilot areas better, this paper adopts a quasinatural experiment of energy-use rights trading (EURT) policy by using the difference-in-difference method from the perspective of cities in China from 2006 to 2019. The results show that the policy of EURT can significantly reduce regional carbon emission intensity, which varies in different regions and different scales of cities. The main goal of implementing the policy is to reduce regional carbon emission intensity by improving the energy consumption structure and promoting the improvement of industrial structure and green innovation. In addition, the spatial impact of the EURT pilot project is demonstrated by its ability to not only reduce local carbon emission intensity but also decrease carbon intensity in and around the designated areas. [ABSTRACT FROM AUTHOR]

China, a country with a long-standing agricultural legacy, is increasingly prioritizing the reduction of CO2 emissions from its agricultural sector. Initially, the carbon emission sources within the agricultural sector are classified into two categories: direct and indirect emissions. Using this classification, the study calculates the generalized agricultural carbon emissions (GACEs) of 30 provinces in China between 2011 and 2020. To further understand the factors influencing GACEs, the paper employs the logarithmic mean Divisia index method and Tapio decoupling index to analyze seven key factors. These factors include carbon emission intensity, energy consumption of generalized agriculture, and economic benefit level of energy consumption. By comparing the impact and changes of GACEs during the 12th and 13th five-year plan periods, the study reveals valuable insights. The findings suggest that carbon emission intensity plays a crucial role in suppressing GACEs, while the level of economic development acts as a catalyst for their increase. By effectively managing these influencing factors, the paper proposes that the increase in GACEs can be effectively suppressed, and the achievement of agricultural CO2 reduction goals can be expedited. [ABSTRACT FROM AUTHOR]

Improving energy efficiency is crucial for achieving the carbon peaking and carbon neutrality goals. The digital economy, which is characterized by big data, artificial intelligence, the internet of things, and a new generation of mobile Internet, has quietly penetrated all aspects of the economy and society, profoundly changing the means of production and lives of human beings. Digital technologies have great potential to improve the global energy system's security, productivity, efficiency, and sustainability. Based on the panel data of 30 provinces in mainland China from 2006 to 2021, this study divided energy efficiency into total and single factor energy efficiency. The two-way fixed-effect model and the Driscol-Kraay method were used to adjust the standard error test in order to examine the impact of digital technology represented by industrial robots on energy efficiency and its path mechanism. Studies have shown that digital technology can significantly improve total factor energy efficiency and reduce energy intensity per unit of GDP. This conclusion was found to be still valid after the robustness test using feasible generalized least squares, time-varying difference in difference and fixed effect space Durbin model. The results of the mechanism test show that digital technology can improve energy efficiency by increasing the degree of industrial virtual agglomeration and the channels of foreign direct investment. This paper provides a valuable discussion on how information technology advances can improve energy efficiency in the era of the digital economy. The conclusions will help relevant market players to formulate policies and measures and corporate strategies to improve energy efficiency. At the same time, it also deepens the theoretical understanding and mechanism path of digital technology's impact on energy consumption. [ABSTRACT FROM AUTHOR]

An analytical hierarchy model of the impact of solar reflectance, thermal emittance, heat transfer coefficient, and heat storage coefficient on building energy consumption was established through the implementation of orthogonal design experiments. The EnergyPlus software (v9.0.1) was utilized to simulate building energy consumption across diverse climatic regions in China, providing essential benchmarks for the orthogonal design. The results of the range analysis consistently indicate that, barring regions characterized by extremely cold climates, solar reflectance emerges as the predominant factor exerting an influence on building energy consumption. As geographical latitude increases, the impact of the heat transfer coefficient becomes progressively larger, while the weight of thermal reflectance concurrently diminishes. Drawing upon the principles rooted in the gradient refractive rate theory and the concept of atmospheric window radiation, a range of high-reflectance and high-emittance cool roof coatings in various colors were meticulously developed. A spectrophotometer was employed to precisely quantify their reflectance properties, and simulations were subsequently conducted to scrutinize their energy-saving characteristics. The results demonstrate that the cool roof coatings that were developed using the methodology described in this paper exhibit substantial enhancements in reflectance, with increases of 0.24, 0.25, 0.37, and 0.35 for the yellow, red, blue, and green cool roofing materials, respectively, in comparison to conventional colored coatings. Under typical summer conditions, these enhancements translate to significant reductions in roof temperatures, ranging from 9.4 °C to 14.0 °C. Moreover, the simulations exploring the cooling loads for the roofs of differing colors consistently revealed remarkable energy savings. These savings were quantified to be 4.1%, 3.9%, 5.5%, and 5.4%, respectively, when compared to conventional coatings of the corresponding colors. These findings offer valuable insights into strategies for optimizing the energy efficiency of buildings through the application of high-reflectance cool roofing materials. [ABSTRACT FROM AUTHOR]

Cold-chain logistics presents significant challenges to sustainable development in terms of economics, society, and the environment. To address this issue, this paper focuses on solving a real-world problem—the multipath location-routing problem-based cold-chain logistics (MPLRPCCL), which considers soft time windows and heterogeneous fleets. The mathematical model aims to optimize the total logistics costs, including fuel consumption, carbon emissions, cargo deterioration, penalties, operating depots, and renting fleets. To effectively solve this problem, the paper proposes three versions of a memetic algorithm (MA) that incorporate control modules for monitoring the MA's performance and improving its global optimization performance. Computational evaluations were conducted, and the results show that the proposed algorithms are effective and outperform the classic MA framework. Furthermore, the proposed model and algorithms were applied to real-world cases in Hangzhou city, China, demonstrating their practical applicability. Overall, this paper offers a promising approach to tackling the complex challenges posed by cold-chain logistics and contributes to the sustainable development of this important field. [ABSTRACT FROM AUTHOR]

In the recent literature, energy efficiency got the attention of scholars due to its discouraging impact on CO2 emissions, which is considered the most prevalent greenhouse gas that human activities produce. Data reports that China is the leading CO2 emitting country across the globe, and still the environmental degradation is in progress. Thus, the current paper empirically investigates the impact of energy efficiency (ENEF), financial inclusion (FD), GDP, export diversification (EXD), and human capital index (HCI) on the environmental degradation of China over the period from 1988 to 2018. This study uses various time-series tests to empirically investigate the determinant of CO2 emissions, including normality tests, unit root tests, and combined cointegration tests. Besides, the long-run coefficients are analyzed via the fully modified ordinary least square (FMOLS), dynamic OLS (DOLS), and the Canonical Cointegrating Regression (CCR) estimators. The empirical findings reveal that all the variables are cointegrated in the long run. However, the coefficient estimate shows that ENEF and HCI significantly promote environmental sustainability. While GDP, FD, and EXD significantly promote environmental degradation by enhancing the CO2 level in the atmosphere. This study recommends practical policy implications based on the empirical findings: energy-efficient products and energy sources could be promoted. [ABSTRACT FROM AUTHOR]