Energy Structure Adjustment Research Articles

The impacts of digital value chain embeddedness on trade-related carbon emissions intensity

Objective: Digitalization supported by digital technology presents a potential solution for improving the efficiency of resource utilization. However, the impacts of digitalization on trade-related carbon emissions intensity have not been studied systematically.Methods: Based on panel data of 41 countries and regions over the period 2000–2014, this study examines how different types of digital value chain embeddedness can affect carbon emissions intensity using a semi-parametric partially linear model.Results: Research findings indicate that there is an inverted U-shaped relationship between digital domestic value chain embeddedness and carbon emissions intensity embodied in domestic trade; only when digitalization reaches a threshold of approximately 0.88, does the effects on carbon emissions intensity become negative. In addition, the impacts of digital global value chain embeddedness on carbon emissions intensity embodied in import trade and export trade are recognized as being non-linear; the thresholds of digitalization are approximately 0.1 and 0.3 for import trade and approximately 0.03 and 0.21 for export trade. Although participating in global value chains is conducive to accelerating digital technology diffusion, the actual environmental effects are constrained by a country’s absorptive capacity and high economic system complexity. Compared with developed countries, developing countries lag behind in entering the downward stage of the inverted U-shaped curve, thereby gaining environmental benefits from digital value chain embeddedness. Moreover, in terms of utilizing digital value chain embeddedness to improve energy efficiency, measures include optimizing trade conditions, adjusting energy structure, and increasing trade scale, which can play an active role.Value: This study sheds light on the exploration of the potential of digitalization and the facilitation of economic development in a more environmentally friendly manner.

Study of Peak Carbon Emission of a City in Yangtze River Delta Based on LEAP Model

Based on the LEAP model framework, a LEAP-X sub-sector calculation model suitable for X City was constructed in this study. Four scenarios including a baseline scenario, low-carbon scenario, enhanced low-carbon scenario, and peak in 2023 scenario were set up to predict and analyze the carbon emission situation. The calculation and analysis results showed that it could achieve the carbon peak before 2030 only under the enhanced low-carbon scenario and peak in 2023 scenario. The peak year of the enhanced low-carbon scenario was around 2025 with a peak carbon emission of approximately 170 million tons, but the peak time may actually be delayed. Industry was the largest sector of carbon emissions, and the petrochemical industry was the largest portion of industry, the proportion of which was always maintained at approximately 30% under different scenarios. However, the proportion of power generation and steel industry decreased annually, whereas the proportion of the net imported power gradually increased. Industrial structure optimization and energy structure adjustment were the main driving factors of carbon peak in X City. Carbon emissions per unit of GDP will fall by approximately 41% in 2030 compared with that in 2020 under the enhanced low-carbon scenario.

Regional productivity in the transformation of China’s energy structure –estimation of the restricted cost function

ABSTRACT In this study, energy structure is introduced into the restricted cost function to study the influence of energy structure transformation (EST) on regional productivity. The analysis indicates that: (i) the average annual productivity of various regions in China has a positive growth, with an average annual growth rate of 6.97% and a downward growth trend from 12.15% to 4.13%. Technological progress has the greatest positive contribution to productivity growth, while energy structure and scale effect have slight negative effects. (ii) Further regional empirical analysis reveals a gradient distributions of eastern > central > western area for productivity growth. Although the effect of technological progress in western area is greater than that of eastern and central areas, low energy efficiency has dragged down its productivity growth. (iii) Factor input bias analysis demonstrates that technological progress tends to factor saving, while energy structure adjustment tends to a slight factor using with different preferences in regions. Inspired by the empirical results, strategies for optimizing policies and regional collaboration are proposed from a systematic perspective.

Study on energy efficiency improvement strategy of photovoltaic-hybrid energy storage DC microgrid considering green energy saving concepts

Abstract In this paper, the development of microgrid technology is discussed in close connection with the growing energy demand and the real needs of energy structure adjustment in the context of the times. Guided by green energy saving, the research focuses on constructing a hybrid energy storage DC microgrid model, especially the integrated photovoltaic power generation model and the related control strategy and power characteristics. The article further adopts a fuzzy PID control strategy to optimize the energy efficiency configuration of hybrid energy storage devices in DC microgrids. By carefully analyzing and optimizing the energy storage system in the DC microgrid, remarkable results are obtained: the optimized capacitive charge states range from 0.1 to 0.9, indicating a significant fluctuation of capacitive charge states, but at the same time avoiding the phenomenon of light abandonment and ensuring the reliability of power supply. More notably, during the increase of SOC (state of the energy storage system) from 0.40 to 0.70, the system can perform mode transitions under continuous load changes. This point shows the effectiveness of the optimization method in improving the energy storage efficiency and enhancing the system performance of the DC microgrid. Overall, the optimization method proposed in this paper provides a practical energy storage optimization scheme for developing microgrids, which is of great practical significance in promoting the research and application of microgrid technology. This not only helps to improve the efficiency of energy use, but also is a crucial step to realize sustainable energy development.

Thermal runaway behaviour and heat generation optimization of the marine battery cabinet based on module thermal analysis

Currently, the application of lithium-ion batteries in electric vehicles has become common in recent years. Considering the adjustment and transformation of the future energy structure, the use of electric ships is increasing; however, the problem of heat production from the battery cabinet of electric ships must be solved. Therefore, in this study, the multi-scale and multi-domain solution method was used to analyse the heat production and heat transfer of a module-level battery to calibrate the basic calculation model in this study. A double-layer cooling arrangement was proposed to optimise the heat production of the model, which controlled the heat production rate of the battery module to be within 0.00497 K/s. Based on the thermal runaway (TR) module, a three-layer marine battery cabinet was visually analysed for the first time, and the influence of TR on the upper and lower layers and the thermal spread behaviour of the battery pack in the middle layer were studied. The results indicated that the temperature change in the battery in the first layer was more significant than that in the third layer. Furthermore, the proposed double-layer cooling scheme controlled the temperature of the battery in direct contact with the heat source to within 324.4 K. The findings of this study provide insights into the TR behaviour of a marine battery cabinet and its influence on heat generation as well as guidance for the thermal management of electric marine battery cabinets.

Carbon Emission and Industrial Structure Adjustment in the Yellow River Basin of China: Based on the LMDI Decomposition Model

In the context of promoting high-quality development in the Yellow River Basin (YRB) of China, urgent action is needed to achieve the “Dual Carbon” goal through energy savings, emission reductions, and industrial upgrading. This study measures carbon emissions from eight types of energy consumption across 43 industries from 2000 to 2019. Using the Kaya-LMDI model, factors affecting carbon emissions are analyzed, and the relationship between industrial structure and carbon emissions is explored through the coefficient of variation (CV). The findings reveal that coal consumption remains significantly higher than other energy sources, and the effect of energy structure adjustment on carbon emission reduction is limited compared to the impact of energy consumption increase on carbon emission growth. Moreover, the economic output effect is identified as the primary driving factor of carbon emissions, while energy utilization rate is crucial in achieving energy savings and emission reductions. Finally, the CV of carbon emissions across 43 industries is increasing. Based on these results, we suggest several policy recommendations, including prioritizing ecological concerns, developing comprehensive and scientifically sound plans, optimizing energy consumption structure, improving energy utilization efficiency, and adjusting industrial structure to promote sustainable development in the YRB.

Analysis of emission evolution and synergistic reduction effect of air pollutants and CO2 from Chinese coal-fired power plants

The coal-fired power industry is the largest coal-consuming sector in China, and it serves as a significant source of air pollutants and carbon emissions. In this study, we investigated the evolution of air pollutants and CO2 emissions originating from coal-fired power plants (CFPPs) from 2013 to 2020 based on a unit-based bottom-up inventory, and then we quantitatively assessed their combined reduction effects from 2013 to 2060. Our findings indicated that air pollutant emissions experienced a rapid decrease from 2013 to 2020, with nearly 46.0%–64.2% of the reduction occurring within the years 2013–2017. However, CO2 emissions displayed a slight increase during the same period. The top five provinces with the most robust synergistic reduction effects for both air pollutants and CO2 were Jiangsu, Hebei, Henan, Shanxi, and Shandong. The primary measures to the reduction in CO2 emissions were energy structure adjustment and energy conservation and consumption reduction. By 2060, emissions of PM2.5, SO2, NOx, Hg, and CO2 had decreased by 57.9–92.1%, 26.0–80.6%, 18.1–69.4%, 26.6–86.3%, and 35.8–76.1% compared to the levels in 2020, respectively. As the emission reduction process advanced, the reduction effect on CO2 became more pronounced within the synergistic process. We believe that our highly detailed emission inventory comprehensively captures the historical evolution of PM2.5, SO2, NOx, Hg, and CO2 emissions from CFPPs. Furthermore, the scenario analysis results we have obtained will be instrumental in supporting policy development aimed at achieving the synergistic reduction of air pollutants and carbon emissions.

Exploring the spatiotemporal evolution dynamic and influencing factor of green ecology transition for megacities: A case study of Chengdu, China

Global climate change, rapid urbanization, and economic development have brought many challenges and threats to human production and lifestyles, increasing the necessity for sustainable human development, urban ecological governance, and low-carbon energy transformation. The current research on the green ecological transition model of global megacities is relatively scattered, with most studies focusing on the green ecological transition of a single dimension of ecological or social value. Based on the panel data of the economic, social, and ecological of Chengdu from 2010 to 2020, we measure the level of green ecological transition of Chengdu and its regions using the entropy weight method and TOPSIS method and then utilize exploratory spatial data analysis to explore the spatiotemporal evolution dynamic of green ecological transition in Chengdu city, and finally use the spatial Durbin model to analyze the key factors of green ecological transition in Chengdu park city. The results showed that: (1) the overall green ecological development level of Chengdu Park City showed an obvious upward trend, and the spatial region presents the characteristics of the circle structure of decreasing from the inside to the outside; (2) the green ecological transformation of Chengdu park cities has shown a positive spatial autocorrelation since 2015, and Jinjiang and Qingyang districts have become the green ecological transformation; (3) the adjustment of energy structure and the improvement of market freedom had a significant positive impact on the development of park cities, with a contribution of 8.3% and 10.9%, respectively. These results and findings will help to provide references for academic research and urban area planning on the green and low-carbon development transition and regional sustainable development of megapark cities.

A novel time-lagged logistic grey model and its application in forecasting energy production volume

Scientific and accurate prediction of energy production is important for exploring energy alternative paths, adjusting energy structure and industrial layout in a targeted manner, and promoting strategic energy transformation. In this paper, a time-lagged logistic grey forecasting model with harvesting term is established by combining the Logistic model, which has the ability of historical recurrence and short- and medium-term forecasting ability, and the grey forecasting model, which has the characteristics of strong adaptability and small computational effort. The least squares method is used to estimate the parameters of the new model, and the mathematical method is used to calculate the time response equation of the new model, and the modeling steps and flow chart of the new model are obtained. Finally, the oil production data of a province in China is used as the validity analysis of the model, and the new model is compared with two classical grey prediction models, three optimized grey prediction models, and one other prediction model. Through the comparison of seven indicators, the results show that the new model is significantly better than other models. Based on the validity analysis results, the new model is applied to the energy production forecast of three typical provinces in China. The horizontal and vertical comparison shows that the model can effectively predict the three kinds of energy production, and can provide technical support for the strategy and measures to adjust the energy security structure and promote the healthy and sustainable development of the energy industry.

Thermodynamic and economic analysis of a novel thermoelectric-hydrogen co-generation system combining compressed air energy storage and chemical energy

With the adjustment of energy structure, the proportion of renewable energy is gradually increasing, and how to solve the problem of renewable energy consumption is becoming more and more prominent. Therefore, a novel thermoelectric-hydrogen co-generation system combining compressed air energy storage (CAES) and chemical energy (CE) is proposed. For energy storage, the system uses adiabatic compression with liquid piston to reduce the generation of compression heat, while using the generated compression heat to preheat the methanol. For energy release, the methanol is fed into the methanol steam reforming reactor (MSR) and the methanol decomposition reactor (MDR) in certain proportions for the hydrogen production reaction; the unreacted methanol and the carbon monoxide produced by the MDR are used as make-up heat for the reactors and the high-pressure air before the turbine, while the exhaust waste heat supplies heat to the customer. The thermodynamic, economic and environmental performances of the system were investigated separately by developing a mathematical model describing the system. The results show that under the design conditions, the system has an energy storage density of 12.00 kWh/m3, an energy efficiency of 88.47 %, an exergy efficiency of 77.04 %, a lifetime net present value of 59.20 M$, a payback period of 4 years, and a CO2 emission per unit of energy output of 227.85 kg/MWh. Increasing the thermostatic heat source temperature and increasing the reactor diameter and length increased the methanol conversion rate. Increasing heat exchange effectiveness of the heat exchanger and heat source temperature can all improve system thermodynamic and economic performance. The energy storage density, energy efficiency, net present value and profitability of the system varied parabolically with the reactor tube diameter. The system energy storage density had a maximum value of 12.03 kWh/m3 at a reactor diameter of 0.040 m.

Fostering sustainability: unveiling the impact of Internet development on carbon emissions in China.

The rapid development of the Internet has significantly impacted various socio-economic activities. Using Chinese Industrial Enterprise database and Industrial Enterprise Pollution database, this research examines the impact and mechanisms of Internet development on CO2 emissions. The key findings are as follows: (1) Internet development has substantially reduced the CO2 intensity of enterprises, and this conclusion remains robust even after performing a series of robustness analyses. (2) The major mechanisms responsible for the reduction in CO2 emissions are productivity improvement, technological innovation, and energy structure adjustment. (3) The analysis of heterogeneity reveals that the effect of Internet development on CO2 reduction is more pronounced in coastal areas, areas with a high share of secondary industry, low-carbon industries, clean industries, small-scale enterprises, and export enterprises. This study provides empirical evidence supporting China's "Internet+" strategy and its progress towards achieving the "Carbon Peaking and Carbon Neutrality Goals."

High-carbon expansion or low-carbon intensive and mixed land-use? Recent observations from megacities in developing countries: A case study of Shanghai, China

Cities have become significant sources of greenhouse gas emissions. Effective land management may be the solution to carbon neutrality targets for megacities with limited land resources. This paper takes Shanghai as a case study to investigate the regional land use dynamics and its impact on carbon emissions following the implementation of land conservation and intensive use policy. During 2010–2020, the land use pattern in Shanghai changed from the previous urban land expansion to a combination of industrial land reduction and woodland expansion. Meanwhile, the area proportion of land-use mixture grids increased from 90.50% to 92.28% with the spatial pattern of mixed types also changing. Furthermore, the notable land-use mixture does not necessarily lead to carbon emission reduction, but it can reduce carbon emission hotspots in industrial agglomerations by promoting the mixed use of industrial land and other land use types. However, megacities cannot achieve carbon balance through land use management alone. Due to the increasing carbon emission density of hybrid industrial land, the joint implementation of a land conservation and intensive use strategy with industrial and energy structure adjustments may be an effective way forward.

Research on the Current Situation and Economic Development Trends of China's Energy Economy Structure: Taking Shandong Province as an Example

Shandong Province is a province with strong economic growth and high energy consumption in China. The energy consumption structure is mainly composed of coal and oil. While the regional GDP is increasing with each passing year, carbon emissions are also increasing year by year, and carbon emissions are mainly concentrated in high energy consuming industries. Coal plays a very significant part in the growth of social economy and has become a necessity for people's production and life. The coal based chemical industry is one of the six leading industries of Shandong Energy Group. The coal chemical industry is a significant component of China's energy construction, and how to improve the quality and efficient utilization of coal chemical products is currently the focus of close attention in the coal chemical industry. Promoting economic transformation and upgrading, as well as energy structure adjustment, is a necessary path for China to achieve the objective of "carbon peaking and carbon neutrality". Carbon peaking and carbon neutrality "is the main foundation and new engine for China's economic and social growth in the next 40 years, which will inevitably drive systematic changes such as low-carbon transformation, structural reshaping, and technological innovation in the energy industry. This article takes Shandong Province as an example to study the current situation of China's energy economy structure and economic development trends.

Analysis of influencing factors of industrial green and low-carbon transformation under the background of “double carbon”: evidence from Sichuan province, China

Introduction: Industrial green and low-carbon transformation is the key to improve economic development and necessary process to achieve the goal of the carbon peaking and carbon neutrality. Few studies have been done on the decomposition of carbon emission factors in industries and sub-industries and the impact of green and low-carbon transformation about carbon emission in each industry quantitatively. However, the study of industries and sub-industries can comprehensively analyze the development path of green and low-carbon transformation from a more detailed perspective, and provide scientific reasons for the optimization of industrial structure and energy structure.Methods: The extended Kaya identity for industrial carbon emission is constructed to obtain four factors influencing industrial carbon emission: economic output effect, industrial structure effect, energy intensity effect, carbon consumption intensity in this paper. Then, the LMDI decomposition method is combined with the above identity to innovatively obtain the contribution value of carbon emissions from the perspective of overall, industrial sector and tertiary industry. Then, based on the results of factor decomposition, a multi-index scenario prediction model is constructed. On this basis, the extreme learning machine model optimized by particle swarm optimization (PSO-ELM) was used to predict the influence of the changes in the driving factors on the reduction of industrial carbon emissions. By setting the baseline and industrial green and low-carbon transformation scenarios, it is predicted that industrial carbon emission in Sichuan Province.Results and discussion: (1) Economic output effect always promotes the growth of industrial carbon emissions, and with the adjustment of industrial structure and energy structure, the other three factors begin to restrain the growth of carbon emissions. (2) Scenario prediction shows that without considering the economic costs of transformation, improving carbon emission reduction efficiency can be obtained through accelerating the rate of change of industrial structure of the secondary and tertiary industries, increasing the proportion of energy intensity reduction, and strengthening the proportion of non-fossil energy use.

Air source assisted ground source composite heat pump system: life cycle carbon emissions and simulation analysis

ABSTRACT Air source assisted ground source composite heat pump (DSHP) system is a combination of ground source heat pump (GSHP) and air source heat pump (ASHP) that makes use of both underground and outdoor temperatures to ensure long-term stability and reliability. The development of a carbon emission model for DSHP system involved a life-cycle assessment (LCA). This model was used to analyze the energy savings of DSHP in two different areas of China Nanjing and Hohhot. In both cases, an office building with an area of 3040 m2 was used as the subject of the study. The evaluation of energy savings using TRNSYS dynamic simulations revealed that the operation stage accounts for about 75%-85% of the carbon emission in the entire life cycle assessment of DSHP systems, Among them, refrigerant leakage produces a carbon footprint of more than 20%. The installation stage accounts for about 10% of the emissions, while the adjustment of the power energy structure during operation is crucial in achieving energy savings and emission reductions. A comparison study shows that DSHP can lower emissions by up to 23% when compared to traditional air conditioning systems like gas boiler and chiller system (BLR-CH). The use of DSHP is therefore a highly effective alternative for organizations committed to reducing their carbon footprint.

Forecast of clean energy generation in China based on new information priority nonlinear grey Bernoulli model.

The forecast of clean energy power generation is of major prominence to energy structure adjustment and the realization of sustainable economic development in China. In order to scientifically predict clean energy power generation data, a structure-adaptive nonlinear grey Bernoulli model submitted to the new information priority criterion (abbreviated as IANGBM) is established. Firstly, an improved conformable fractional accumulation operator that conforms to the priority of new information is proposed, which can effectively extract the information from small samples. Then, IANGBM is derived from the Bernoulli differential equation, and the perturbation bound theory proves that this model is suitable for the analysis of small sample data. In addition, the grey wolf optimization algorithm is utilized to optimize the model parameters to make the model more adaptable and generalized. To verify the superiority of the model, two cases consisting of wind and nuclear power generation prediction are implemented by comparing eight benchmark models involving IANGBM, GM, FGM, FANGBM, LR, SVM, BPNN, and LSTM. The experiment results demonstrate that the proposed model achieves higher prediction accuracy compared to the other seven competing models. Finally, the future nuclear and wind power generation from 2023 to 2030 are predicted by adopting the IANGBM(1,1) model. For the next 8 years, nuclear power generation will maintain stable development, while wind energy power generation will grow rapidly.

The impact of carbon emission trading policy on energy efficiency-evidence from China.

Energy efficiency (EE) plays an important role in achieving the dual-carbon goal, and improving EE is thus indispensable. This paper evaluates the impact of carbon emission trading policy (CETP) on EE based on a difference-in-difference (DID) method, using 16-year data of 30 provinces and cities from 2005 to 2020. Conclusions are as follows: (1) CETP significantly promotes EE, and this conclusion still appears valid after robustness tests. (2) The positive impact of CETP on EE is more effective in regions of high foreign direct investment (FDI) and high government intervention (GOVI). (3) The positive impact of CETP on EE is through impact mechanisms of energy structure adjustment (ESA), green innovation (GI), and industrial structure upgrading (ISU). The findings in this paper may enrich current research in CETP and offer more pragmatic suggestions for policy advancement as well.

Research on Regional Carbon Emission Reduction in the Beijing–Tianjin–Hebei Urban Agglomeration Based on System Dynamics: Key Factors and Policy Analysis

Urban agglomerations are regions where the economy and population are highly concentrated, which are also spatial units with more concentrated carbon emissions. A detailed decomposition of driving factors based on changes in carbon emissions of urban agglomerations can provide a reference for better carbon reduction policies. In this paper, we establish an evaluation framework of carbon emission drivers of urban agglomeration from the perspective of CO2 generation and removal using a system dynamics method. The key influencing factors and optimal emission reduction measures of carbon emissions in urban agglomerations are explored. The results are as follows: (1) The industrial structure is the key influencing factor of carbon emissions; (2) compared with no implementation of any policies, the total carbon emissions and carbon emission intensity of integrated policies all significantly decrease, with a decrease of 43.68% and 53.32%, respectively in 2035; (3) energy structure adjustment has a significant effect in reducing carbon emissions and carbon emission intensity; (4) the role of increasing investment in technological innovation in ensuring achievement of “carbon peak” should not be ignored. It is found that integrated policies often exhibit a better emission reduction effect, but this effect is not a simple summation of the effects of each single policy.

A task matching model of photovoltaic storage system under the energy blockchain environment - based on GA-CLOUD-GS algorithm

Photovoltaic storage system (PVSS) has been spawned with the combined application of photovoltaic (PV), energy storage (ES) and energy blockchain (EB), which has also made important contributions to the energy structure adjustment, energy transaction security and ecological environment protection. The establishment of a reasonable task matching mechanism can further improve the operation efficiency and quality of the system under the EB environment, and this paper consequently constructs a task matching model of PVSS based on GA-CLOUD-GS algorithm. Firstly, the decision-making committee is set up and the index system is determined for the to-be-matched task. Secondly, the Genetic Algorithm (GA) comprehensive weighting method is introduced to integrate the subjective and objective weights to achieve the weight optimization. Thirdly, the cloud model is used to obtain the preference order and priority order of the to-be-matched subjects. Fourthly, the Gale-Shapley (GS) algorithm is used to obtain the task matching results. Finally, the operability, the stability and the effectiveness of the model are separately verified through numerical simulation, sensitivity analysis and comparative analysis. Corresponding suggestions for the task matching of PVSS in the EB environment are also put forward according to the research content.

Collaborative optimization of intersectional angle and installation height of branched fin for ameliorating melting characteristics of lowly thermal conductive phase change materials (PCMs)

Energy storage systems based on phase change materials (PCMs) have become reliable methods to alleviate energy shortages and adjust energy structure. Optimizing fin structure parameters has a vital impact on improving the equivalent thermal conductivity of PCMs and enhancing thermal charging performance. In this numerical investigation, collaborative optimization of intersectional angle and installation height of branched fin on melting of organic PCMs have been systematically studied to coordinate transient thermal conduction and natural convective heat transfer thus ameliorating melting characteristics. Results clarify that optimizing intersectional angle between adjustable fin and horizontal direction can shorten total melting time by 9.39% and ameliorate the average heat transfer rate during melting by 11.07% compared to the case of 0o intersectional angle. These improvements are dependent of installation height of adjustable fin. Meanwhile, average heat transfer rate during melting induced by optimizing intersectional angle is enhanced by 2.77% compared with that caused by optimal orthogonal fins. Moreover, optimum intersectional angle is significantly depended on installation height. A linear correlation between optimum intersectional angle and installation height is obtained. The reliability and rationality of the correlation is confirmed. Effective guidelines for designing optimum branched fin to improve melting performance of organic PCMs are consequently acquired.