Energy Structure Optimization Research Articles

Multi-objective optimization of non-fossil energy structure in China towards the carbon peaking and carbon neutrality goals

Numerous nations and regions are earnestly dedicated to propelling energy transition. China accounts for 31.78 % of the total global carbon emissions, and China's energy structure holds paramount significance in the broader context of global initiatives. China has officially proposed to achieve the target of carbon peak by 2030 and carbon neutrality by 2060, imposing substantial pressure on the transformation of the energy structure. In this study, we propose a multi-objective optimization model for the non-fossil energy structure, encompassing three primary optimization objectives: maximizing power generation, minimizing economic costs, and reducing CO₂ emissions. The optimization involves determining the proportion of seven key non-fossil energy sources—hydropower, onshore wind, offshore wind, photovoltaic (PV), bioenergy, geothermal, and nuclear power—while adhering to constraints related to power demand, nuclear safety, and other factors. The analysis explores the installed capacity, power generation, power generation costs, and CO₂ emission reductions of non-fossil energy under distinct scenarios in 2025, 2030, and 2060. The findings reveal that by 2030, the optimal share of power generation should be sequenced as onshore wind, hydropower, nuclear power, offshore wind, PV, bioenergy, and geothermal. Subsequently, by 2060, the top three developed power should be onshore wind, nuclear power, and hydropower.

Index System of Low-carbon Driving Factors of Construction Industry in Hebei Province

Under the background of "double carbon" goal, the low-carbon development of construction industry in Hebei Province has become an important part of achieving the carbon peak in 2030 and carbon neutrality in 2060. The construction industry is one of the main sources of carbon emission in Hebei Province, and how to promote its low-carbon development is an urgent issue to be solved. Based on the five aspects of technology, energy, society, economy and industry, this paper constructs the low-carbon driving factor index system of the construction industry. The research shows that technological innovation, energy structure optimization and economic structure adjustment are the key factors to promote the low-carbon construction industry. In addition, by analyzing the current situation and future trend of carbon emissions of the construction industry, this paper puts forward corresponding policy suggestions, which provides theoretical support for the government to formulate countermeasures for the low-carbon development of the construction industry. The conclusion points out that the research and development and application of low-carbon technology, the optimization of energy structure and the green transformation of industrial structure will be the core path to realize the low-carbon development of the construction industry.

Analysis of New Energy Generation Technology Based on the "Dual Carbon" Goal

With the increasingly severe global climate change issues, achieving the "dual carbon" goal has become a common pursuit for China and the world. The development of new energy power generation technology is of great significance for reducing greenhouse gas emissions and optimizing energy structure. This article first outlines the background and significance of the "dual carbon" goal, and analyzes its driving effect on new energy power generation technology. Subsequently, this paper discusses in detail several mainstream new energy power generation technologies, including wind energy, solar energy, biomass energy, and geothermal energy, and systematically analyzes their development status and challenges. In the part of technological innovation, this paper focuses on the application and innovation trend of smart grid technology, energy storage technology and power electronics technology in the field of new energy power generation. In addition, this paper also evaluates the economy of new energy power generation technology, including cost-benefit analysis and the impact of policy environment. Finally, this paper summarizes the research results and puts forward the direction and suggestions for future research.

Regional energy consumption policy practice to promote low-carbon economy

IntroductionEnergy is the fundamental driving force for world economic and social development. However, the growth of carbon dioxide emissions and increasing environmental challenges are becoming increasingly problematic, limiting the pace of economic development.MethodsTo optimize the energy consumption structure and promote the development of low-carbon economy, this study took Shaanxi Province as an example to analyze the impact of low-carbon economy policy variables on the energy consumption structure dominated by coal. The purpose of this study is to examine the influence of policy variables on the evolution of energy consumption structure by constructing a system dynamics model. In addition, this study will explore policy optimization issues.ResultsThe error of simulation results was within ±10%. The consumption of non-fossil energy increased from 2,196,400 tons of standard coal in 2009 to 22.578 million tons in 2030, which is in line with the development trend. Under these four policy combinations, the share of coal will fall to 57.8 percent by 2030. In addition, the share of non-fossil energy sources will increase to 21 percent and the share of natural gas will reach 16 percent.DiscussionThe policy suggestions put forward in this paper are of great significance to the optimization of energy structure led by coal. The model constructed can be effectively applied to practical policy simulation and adjustment, and can provide reference for the development of low-carbon economic policies in other regions.

Job destruction or job creation?: Evidence from carbon emission trading policies

Based on the panel data of 285 cities in China from 2007 to 2019, this paper constructs a theoretical framework to analyze the employment effect of carbon emission trading policy, and quantitatively evaluates the effectiveness, decomposition effect and influence mechanism of carbon emission trading policy on employment based on PSM-DID method. The study shows that the carbon emissions trading policy has a positive impact on the level of urban employment while producing significant environmental governance effects, and that its positive impact on employment in the pilot cities stems from the negative effect of reducing employment destruction rather than from the positive effect of increasing the job-creating effect. Carbon emissions trading policy promotes employment in the pilot cities through structural transformation effects, specifically through the three paths of green technology innovation, energy structure optimization and industrial structure upgrading. At the same time, the impact of the policy on employment varies significantly across cities of different locations, sizes and resource endowments. The study proposes that the construction of the national carbon market should be accelerated, a market-oriented green technology innovation system should be constructed, the optimization of the energy structure and the upgrading of the industrial structure should be promoted, and training in employment skills should be strengthened. The theoretical analysis of this study provides a theoretical basis for the micro-mechanism of carbon emissions trading policy, and the empirical results provide practical reference for the change of employment structure and the high-quality development of employment.

Combining life cycle assessment and dynamic qualitative comparative analysis to analyze the environmental impacts of urban development across different provinces in China

Examining the environmental pollution landscape is a vital link for urban development. Across production, distribution, and usage stages of 24 provinces in China between 2010 and 2019, this study conducts Life Cycle Assessment and Dynamic Qualitative Comparative Analysis to explore the effects of conditional combination on environmental impact assessment indicators including Photochemical Oxidation, Eutrophication, Global Warming Potential, Human Toxicity, and Acidification. The results reveal that: (1) Shandong contributes most significantly to environmental impact assessment indicators in the production and distribution stages, while Inner Mongolia dominates the usage stage. In contrast, Beijing, Hainan, and Shanghai show lower contributions, though Hainan exhibits a rising trend in the Global Warming Potential index across all stages; (2) The production stage consistently accounts for the largest environmental impact, with notable differences in contributions from Beijing, Ningxia, Inner Mongolia, and Shanghai across the three stages; (3) Environmental impact is not driven by individual factors alone; rather, it is the combination of factors that proves influential. Complex nonlinear relationships are revealed between energy consumption patterns and environmental impacts in the production stage. The combination of transport operating distance and traffic volume has an interaction during the distribution stage. The usage stage leads only to the environmental impact of Eutrophication, which is caused by the resident population and ecological water consumption excluding residential water use. These findings present a new strategy for optimizing energy structure and water resource utilization, and emphasize the synergies to reduce the environmental impacts, providing instrumental implications for policymakers and government managers in environmental management and urbanization development.

Spatiotemporal evolution and driving factors of China's carbon footprint pressure: Based on vegetation carbon sequestration and LMDI decomposition

Evaluating carbon footprint pressure (CFP) scientifically and quantifying the environmental impact of economic activities precisely are crucial for informing policies on carbon emission reduction and achieving carbon neutrality in China. This study revealed China's CFP from 2005 to 2021 using data sets of fossil fuel carbon emissions and vegetation carbon sequestration from remote sensing. We investigated the drivers of CFP using LMDI decomposition. Additionally, we used the Tapio model to examine the decoupling of CFP and economic growth. The findings indicate that China's CFP has increased over time and shows a positive spatial correlation. Rapid economic growth and significant population expansion are the primary drivers of the surge in CFP. Conversely, optimizing energy structure, reducing energy intensity, and enhancing ecological quality are key to alleviating the rising pressure on China's carbon footprint. Moreover, we observe a weak decoupling of China's CFP and economic growth. Notably, it is more challenging to achieve an ideal decoupling state in the western region than in the central and eastern regions.

Impact of Big Data on Carbon Emissions: Empirical Evidence from China’s National Big Data Comprehensive Pilot Zone

Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data.

Aluminum demand and low carbon development scenarios for major countries by 2050

Aluminum is a widely used energy-intensive material and the second largest source of carbon emissions from metal after steel. Global demand for aluminum has grown rapidly since 1970 and has shown a high correlation with population and economic growth. These factors have posed challenges to carbon reduction of aluminum industry. Therefore, it is crucial to investigate global aluminum demand and emissions to develop low carbon pathways. However, few studies have conducted a comprehensive analysis of the global aluminum industry. We develop different scenarios of joint abatement measures to explore pathways for the aluminum industry to significantly reduce carbon emissions by 2050. Three scenarios are considered, namely, moderate mitigation scenario (MMS), enhanced mitigation scenario (EMS), and deep mitigation scenario (DMS). The results show that: 1) Aluminum industry has a high potential for carbon emission reduction. The emissions in the three scenarios will be reduced by 33%, 77% and 85% by 2050 compared to 2020, respectively. 2) The carbon emission reduction effect of energy structure optimization and low carbon technology application is significant, with the use of clean energy playing a decisive role. 3) There is an inverse U-shaped curve relationship between per capita primary aluminum demand and per capita GDP. The aluminum demand in China and India is still growing at a high rate, and carbon emissions are always at the top. This study will effectively support the development of a low-carbon transition path for the global aluminum industry.

The energy absorption characteristics and structural optimization of titanium/UHMWPE fiber metal laminates under high-speed impact

Fiber-metal laminates (FMLs), known for their lightweight and high strength, are widely used in structural protection in the fields of shipbuilding, military, and aerospace. Experiments were conducted using 12.7 mm hard spherical projectiles at speeds ranging from 915.7 – 1290 6 m per second to study the high-speed impact on FMLs composed of titanium and Ultra-high Molecular Weight Polyethylene(UHMWPE). The primary failure modes of the fibers were tensile failure and compressive shear failure. With increasing impact velocity, the proportion of tensile failures in the fibers gradually decreased, transitioning to shear plug failure as the main failure mode, while the titanium alloy primarily experienced erosive perforation and petal-shaped tearing. At a speed of 1290 6 m/s, the titanium alloy began to exhibit significant adiabatic shear tearing in four directions. Further, a three-dimensional numerical model was established, which, through theoretical analysis and experimental validation, proved to be highly reliable. Using this theoretical model, a deeper analysis of the dynamic response and penetration mechanism of the structure was conducted, explaining the energy distribution mechanism and dynamic response mechanisms of various parts. Based on this model, improvements and optimizations were made to the laminar structure of the UHMWPE/titanium alloy FML. Placing metal at the back maximized energy absorption but led to more pronounced bulging.

A Study on the Impact of China’s Prefabricated Building Policy on the Carbon Reduction Benefits of China’s Construction Industry Based on a Difference-in-Differences Method

The construction industry is a significant contributor to carbon emissions in China. To effectively meet the “dual carbon” targets, several provincial regions within the country started to implement policies promoting prefabricated buildings. This study examines data from 18 provinces in China over the period from 2012 to 2021, treating the introduction of prefabricated building policies as a quasi-natural experiment. Utilizing the difference-in-differences methodology, this research assesses the impact of these policies on the carbon emission performance of China’s construction sector and evaluates the robustness of the findings. The results indicate that the prefabricated building policies positively influenced the carbon emission efficiency of the construction industry. Specifically, these policies enhance carbon emission efficiency by increasing labor productivity, optimizing the allocation of mechanical resources, and improving the utilization rate of building materials. Additionally, the effectiveness of these policies is positively correlated with the level of regional technological innovation, environmental protection efforts, and the advancement of energy structure optimization. The study concludes with several policy recommendations aimed at further enhancing the effectiveness of prefabricated building policies.

Megacity pathways in China under the dual carbon goal: The case of Shanghai

The pathways to achieving carbon neutrality at the city level are diverse due to varying energy supply and demand conditions. Shanghai faces obstacles such as limited land resources, high costs of renewable energy technologies, and instability of renewable energy. These challenges hinder the city’s efforts to achieve carbon peak and carbon neutrality (dual carbon). Therefore, Shanghai must identify and optimize its development path for renewable energy under the dual carbon goal. We employed the Low Emissions Analysis Platform-Shanghai (LEAP-SH) model to simulate the impact of policies, such as industrial upgrading, energy efficiency improvement, energy structure optimization, increased technical innovation on energy, and ecological restoration, on the carbon emission pathways from 2022 to 2060 using five different scenarios. Our results indicate that Shanghai has the potential to achieve carbon neutrality in 2059 by promoting carbon reduction, pollution control, and green expansion. Moreover, we determined that the manufacturing industry; power generation industry; and transportation, storage, and mail services are the three major sectors for emission reduction under the dual carbon goal. Furthermore, the capacity and output of coal-fired power plants will be gradually replaced by offshore wind power in the dual carbon pathway. Finally, this study proposes countermeasures and suggestions for Shanghai to attain the dual carbon goal and high-quality development.

Can Artificial Intelligence Effectively Improve China’s Environmental Quality? A Study Based on the Perspective of Energy Conservation, Carbon Reduction, and Emission Reduction

The “technological dividends” brought by AI development provide a new model for the country to achieve green governance, enhance enterprises’ ability to manage pollutant emissions during production and operations, and create a new driving force for improving environmental quality. In this regard, this paper systematically examines the impact of AI on environmental quality in China by employing provincial panel data spanning from 2000 to 2020. Focusing on energy conservation, carbon reduction, and emissions mitigation, the analysis is conducted through the application of a two-way fixed-effects model and mediation effects model to explore both the effects and the mechanisms of AI’s influence on environmental quality. The findings indicate that the development and implementation of AI contribute positively to China’s efforts in energy conservation, carbon reduction, and emissions mitigation, ultimately leading to an enhancement in environmental quality. This conclusion remains valid after multiple robustness checks. Mechanism tests reveal that the optimization of regional energy structures, advancements in green technological innovation, and upgrades in industrial structures serve as crucial pathways through which AI facilitates energy conservation, carbon reduction, and emissions mitigation. Heterogeneity analysis uncovers a notable “path dependence” effect in China’s AI development; regions characterized by higher material capital investment, more advanced technological market development, and greater levels of marketization experience a relatively more pronounced impact of AI on the enhancement of environmental quality. This study offers direct references and practical insights for countries globally to foster AI development, enhance environmental quality, and advance high-quality economic growth amid the ongoing wave of digital and intelligent transformation.

Impact of Industrial Intelligence on China’s Urban Land Green Utilization Efficiency

Against the backdrop of the fourth technological revolution, industrial intelligence (INDI) represented by industrial robots has rapidly developed. This evolution provides favorable opportunities for precise decision-making in pollution control and achieving China’s “dual carbon” goals. Previous studies have mainly discussed the economic effects of INDI from the perspective of the labor market. This study shifts its focus to examining the impact of INDI on the land green utilization efficiency (LGUE) in cities. Using the panel data of Chinese cities spanning 2009–2021, this study empirically tests the effect and transmission mechanism of INDI on LGUE. We find that urban INDI significantly enhances LGUE. In terms of its transmission mechanism, INDI drives improvements in urban LGUE through technological progress, energy structure optimization, and industrial structure upgrading. Urban infrastructure construction and financial agglomeration level can further strengthen the positive impact of INDI on LGUE. In addition, the improvement in LGUE due to INDI is more significant in non-resource-based and large-sized cities than resource-based and small and medium-sized cities. Therefore, each region should enhance the integration of intelligent technology with traditional industrial manufacturing. Doing so is essential to establish comprehensive assessment indicators that balance environmental protection and economic growth, strengthen regional information infrastructure construction, ensure steady financial flow, and support green development initiatives across regions.

Assessing the Environmental and Economic Impacts on Industry Informatization for Ningxia’s Renewable Energy Development

This paper analyzes the informatization process of Ningxia's renewable energy industry, its impact on the environment and economy, and uses the coupling coordination degree and grey correlation method to construct a system covering government policies, technological progress, new energy development trends, natural environment and other factors, which can comprehensively evaluate the informatization process in Ningxia and the impact on local renewable energy. Firstly, the coupling coordination degree is used to calculate the relationship between industrial information and renewable energy, and then the gray correlation analysis method is used to evaluate the impact index of Ningxia's renewable energy industry informatization, and corresponding suggestions are taken. The results of the model analysis show that the informatization of Ningxia's renewable energy industry can have a positive impact on the environment and economic development, and there is a strong correlation between the informatization process of Ningxia's renewable industry and government support, Ningxia's economic level, and natural environment quality. The process of informatization is closely related to government policies, technological progress, new energy development trends, and environmental factors, and the optimization of the new energy structure can improve the sustainability of Ningxia's economy, so improving the above content can promote the development process of renewable energy in Ningxia.

“Carbon” suppresses “energy” - How does carbon emission right trading policy alleviate the energy trilemma?

Against the backdrop of addressing the dual challenges of tightening energy constraints and carbon emission reduction, this paper, building upon the construction of an urban-level Energy Trilemma Index, employs a Difference-in-Differences (DID) model to assess the impact of carbon emission right trading policy (CERTP) on the energy trilemma (ET). Key findings include: Firstly, compared to non-pilot areas, the ET in CERTP pilot areas decreased by 1.86 %; the policy's impact persists and is even greater in the long term, reaching 2.84 %. Secondly, CERTP stimulates green technological innovation, improves energy consumption structures, and alleviates ET levels. Furthermore, the study shows that CERTP, as a market-driven energy-environmental strategy, is further enhanced by the regulatory effects of marketization and government intervention. Lastly, the policy's impact is more pronounced in eastern China, resource-based cities, regions with stringent environmental regulations, and areas with high carbon prices. Based on these findings, the paper proposes a series of policy insights, not only aiding in understanding the causal relationship between CERTP and ET but also providing valuable references for designing carbon trading mechanisms to address ET. Importantly, the conclusions offer beneficial guidance for China's energy structure optimization, ensuring energy security and sustainable development goals.

Energy structure optimization of shandong province by 2035 under double carbon goal

Abstract In this paper, based on the existing population size, economic development level, industrial structure, current status of energy production and consumption, and current status of electricity production and consumption in Shandong Province, the LEAP-Shandong model was constructed by using the Long-range Energy Alternatives Planning (LEAP) model as a research tool, which is used to forecast the population size, GDP, energy consumption, and electricity structure of different industries in Shandong Province up to 2035 under three scenarios: policy development scenario, energy efficiency improvement scenario, and enhanced low-carbon scenario. The result shows that the population of Shandong province is expected to peak at 10, 426, 000 by 2033, and then slowly decline; in the enhanced low-carbon scenario, with an appropriate slowdown in economic development, further optimization of industrial structure and enhancement of energy efficiency can effectively reduce the energy consumption of the terminal energy-using sector in Shandong province. Based on the results, the main recommendations for Shandong’s energy development are as follows: to build a low-carbon industrial system in Shandong in light of new development opportunities; to build a new energy development system in light of Shandong’s energy characteristics; and to promote the low-carbon transformation of Shandong’s energy in light of the new model of the energy industry.

Novel STAttention GraphWaveNet model for residential household appliance prediction and energy structure optimization

The multifaceted challenge of carbon dioxide (CO2) emissions during building operations, construction, and material production perpetuates energy shortages and climate adversities. Urgent imperatives underscore the need for effective energy reduction and carbon mitigation, driving the building sector toward a paradigm shift in clean, low-carbon evolution. However, the complex multidimensional and nonlinear characteristics of influencing factors of building energy consumption make it difficult in precisely understanding the spatial and temporal relationships between the features in the predictive modeling of buildings. Therefore, a novel Graph Wavenet (GWN) based on the spatio-temporal attention fusion mechanism (STA-GWN) is proposed for spatio-temporal graph prediction modeling of structures, which can capture hidden spatial relationships using an adaptive dependency matrix. To overcome the drawbacks of standard predefined adjacency matrices and their inability to capture faraway dependencies, a spatio-temporal attention fusion mechanism is introduced to extract latent spatio-temporal features dynamically. By combining extracted features, building energy consumption can be predicted more precisely. Moreover, the proposed model becomes more interpretable, making it easier to reveal how spatio-temporal features relate to building energy consumption. Lastly, the proposed method is applied to energy conservation and emission reduction in the building industry. A predictive model for electrical energy consumption during two public datasets with an operational phase of buildings is established to analyze and optimize energy utilization in residential structures that rely on electricity. The experimental results demonstrate that the STA-GWN model surpasses other deep learning methods in terms of prediction accuracy and robustness. Moreover, the proposed model can offer optimal solutions for building energy processes, reducing approximately 201.44 kg (Kg) and 64.08 Kg of CO2 emissions in the appliances energy prediction dataset and the individual-household-electric-power-consumption (IHEPC) dataset, respectively.

Navigating urban sustainable development: Exploring the impact of low carbon policies on the urban ecological carrying capacity

Amidst a backdrop of decelerating economic growth, China confronts mounting environmental challenges, including air pollution, water scarcity, and escalating carbon emissions. In this context, enhancing the urban ecological carrying capacity (UECC)– defined as the ability to sustain human activities without compromising environmental quality – emerges as a pivotal concern. To address these issues, the Chinese government has instituted the “Low Carbon Pilot Cities policy” initiative (LCPP), aiming to foster sustainable urban development by encouraging cities to adopt measures that reduce carbon emissions and enhance energy efficiency. This paper treats the policy as an exogenous quasi-natural experiment, employing a spatial double-difference methodology to assess its impact on the ecological carrying capacity of 281 Chinese cities from 2006 to 2021. Utilizing the pressure-state-response (PSR) model, this study constructs and evaluates their ecological carrying capacity. The findings reveal that: firstly, the low-carbon pilot policies significantly augment urban ecological carrying capacities; secondly, the policy's effects exhibit regional variability; thirdly, these policies bolster ecological carrying capacity through innovation-driven and energy structure optimization mechanisms; and fourthly, a further spatial spillover analysis indicates that the policy implementation adversely affects the ecological carrying capacity of adjacent cities. Consequently, this study offers empirical evidence and policy insights to assist countries in judiciously implementing environmental policies to achieve sustainable development objectives.

Research on the impact of green logistics on carbon emission intensity of transportation industry

<p>With the serious challenge of global climate change, the Chinese government has proposed the "dual carbon" goals. In this context, the importance of green logistics as an effective way to reduce emissions in the transportation industry is becoming more and more prominent. This paper aims to explore the impact of green logistics on the carbon emission intensity of the transportation industry, in order to provide reference for relevant policy formulation and industry practice. Based on quantitative research methods, the development level of green logistics in 30 provinces in China is quantified using the entropy method, followed by an examination of the impact of green logistics on carbon emission intensity within the transportation industry through the construction of a benchmark regression model. The findings suggest that implementing green logistics can significantly reduce carbon emission intensity, with energy structure optimization being one channel for achieving this reduction. Additionally, improving information technology levels through green logistics can also help lower carbon emissions. Regional heterogeneity tests indicate that green logistics has a significant inhibitory effect on carbon intensity in western and northeast regions, while it is not as effective in central and eastern regions. Therefore, China should continue developing green logistics while considering regional differences and industry characteristics to achieve low-carbon development within the transportation industry.</p>