Optimization Of Consumption Structure Research Articles

Energy, economic, and environmental impacts of electricity market-oriented reform and the carbon emissions trading: A recursive dynamic CGE model in China

Electricity market-oriented reform and carbon emissions trading, as the main policies implemented in China's power sector, have a profound impact on China's green and low-carbon transition by promoting resource allocation efficiency and internalizing external environmental costs, respectively. Therefore, this paper constructed a dynamic recursive CGE model to measure the energy, economic, and environmental impacts of carbon emissions trading and electricity market-oriented reform from electricity market structure and pricing mechanism. The results show that the two policies have a synergistic effect on industrial structure optimization, energy consumption structure optimization, and carbon emission reduction. Specifically, the share of value-added of the tertiary sector, the growth rate of coal consumption, the share of thermal power consumption, and carbon emissions in 2030 are reduced by 5.70 %, 18.29 %, 2.55 %, and 2.838 trillion tons, respectively, compared with the scenario without carbon emissions trading and electricity market-oriented reform. However, imperfectly competitive market structures outperform perfectly competitive market structures in energy consumption structure and carbon emission reduction. Electricity price regulation is also more beneficial to GDP growth and increase in residential disposable income compared to price liberalization. To this end, returning emission trading scheme revenues to residents can effectively mitigate the adverse impacts caused by market-oriented reforms.

The Optimal Path for China to Achieve the “Dual Carbon” Target from the Perspective of Energy Structure Optimization

Exploring the path of energy structure optimization to reduce carbon emissions and achieve a carbon peak has important policy implications for achieving the “Dual Carbon” target. To this end, this paper explores the optimal path for China to achieve the “dual carbon” target from the perspective of energy structure optimization in three steps: (1) we forecast China’s carbon emissions and carbon intensity during 2024–2035 based on a combined forecasting model; (2) we simulate the development of energy consumption and carbon emissions under the “economic development scenario-energy structure scenario” with the help of Markov chain forecasting model; (3) we construct a multi-attribute decision model to account for the above elements as variables to calculate a composite index to analyze the optimal path for China to achieve “Dual Carbon” target under different decision preferences. It is found that (1) potential negative effects caused by COVID-19 are not as serious as reported; (2) only the scenario with low-speed economic growth and effective policies guiding, which doesn’t follow laws of social development, can contribute to reaching carbon peaking by 2030 while maintaining a high-quality carbon intensity; (3) the optimal path that scenario with middle-speed economic growth and strict cost control is a sub-optimal choice subject to realities; (4) technologies innovations in green or low-carbon fields are needed to accelerate energy consumption structure optimization.

Can green finance development abate carbon emissions: Evidence from China

Green finance has been treated as an important tool in the transformation process to low-carbon economy; however, it still remains a question whether the development of green finance could offer carbon savings, or inversely lead to more carbon debts. Based on panel data of 30 provincial administrative regions during 2008–2019, we use the fixed-effect model to examine the carbon emissions reduction effect of green finance. The empirical results suggest that green finance development exerts significantly negative influence on carbon emissions by energy consumption structure optimization and industrial structure upgrading. Besides, we also find that compared with green bond, green credit has a more pronounced carbon emissions reduction effect at present. Furthermore, the heterogeneity of green credit types and regional heterogeneity are both discussed in this study. It is indicated that the green credit oriented for energy conservation and environmental protection could restrain the carbon emissions more significantly while the green credit oriented for clean energy cannot contribute to cutting down carbon emissions as remarkably as other types of green credit. As for regional heterogeneity, results suggest that green finance could curtail carbon emissions in the central and western regions, while this effect is not significant for provinces in the eastern part of China. Thus, the green finance policies should be implemented based on regional characteristics in order to abate carbon emissions more effectively.

The Development of Digital Economy Boosts Consumption Upgrading

The rapid development of digital economy has had a tremendous impact on China's economic structure and residents' lives. Based on the macro vision of accelerating the construction of a new development pattern, expanding domestic demand and boosting consumption upgrading are the current and future policy orientations for a long time. Therefore, on the basis of theoretical analysis, based on the data of China Digital Economy Index and China Family Panel Studies (CFPS) from 2012 to 2018, this paper constructs a Two-way FE model to investigate the relationship between the development of digital economy and consumer consumption upgrading. The empirical research results reflect that promoting the development of digital economy can effectively promote the upgrading of residents' consumption. The upgrading of residents' consumption is mainly reflected in the improvement of consumption level, optimization of consumption structure and improvement of consumption quality. In this paper, the instrumental variable method is used to solve the potential endogenous risk, and the robustness analysis proves that the empirical results are robust for different indicators. Based on the research conclusion, this paper suggests that the relevant government departments can consider appropriate tilt of resources to promote the development of the local digital economy, accelerate infrastructure construction, strengthen the training of scientific and technological talents, increase market supervision and guidance, and then play the role of digital economy in promoting consumption upgrading.

Does Infrastructure Improve Residents’ Consumption? Evidence from China’s New‐Generation Infrastructure

The new infrastructure is becoming the key support and important material guarantee for a new round of scientific and industrial changes in China. In recent years, the further upgrading of the scale and structure of resident consumption has faced resistance from both the supply and demand sides. By enhancing the consuming environment, enhancing transaction efficiency, and lowering spatial barriers, among other benefits, new infrastructure could encourage improvements in resident consumption level and the optimization of consumption structure. This research empirically investigates the consumption implications of new infrastructure under two standards using panel data from 30 Chinese provinces from 2003 to 2020. The findings demonstrate that new infrastructure significantly affects the expansion and upgrading of consumption. Its influence is quite heterogeneous. Additionally, it is discovered during the mechanism of influence test that improving the industrial structure might have a considerable impact on the consumption effect of new infrastructure. In the context of China’s economic transition to high quality, this paper aims to quicken the upgrading of industrial structure and optimize the layout of new infrastructure investment in order to increase consumer returns from infrastructure and support consumption upgrading.

Retracted: Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.

[This retracts the article DOI: 10.1155/2022/3029528.].

Consumption structure optimization for reducing energy footprint

To investigate how to obtain the optimal balance between energy consumption and economic development, this paper proposes a multifactor optimization model (MFOM). Different from previous input–output optimization models, our proposed MFOM comprehensively considers the direct and indirect impacts of sectoral consumption activities on total energy consumption. The results show that MFOM achieves higher energy-saving goals by smaller adjustment to consumption activities than the previous input–output optimization models do. The results also show that the consumption of some products should be limited to achieve the energy-saving and economic growth goals, such as Non-ferrous Metal Ore Mining, Chemical Products and Ferrous Metal Ore Processing. Furthermore, the results indicate that the share of some sectors, especially high-level manufacturing sectors, significantly decreases in the consumption structure after optimization. To maintain the sustainable development of these sectors, the dependence of their production activities on energy-intensive products should be reduced.

How can Chinese metropolises drive global carbon emissions? Based on a nested multi-regional input-output model for China

Given metropolises' participation in complex regional and global trade networks, they have huge demands for vast carbon-embodied intermediate and final goods. To clarify embodied carbon transfers of metropolises in regional and international trade, a metropolis-centered model was constructed by nesting the World's multi-regional input-output table and China's multi-regional input-output (CMRIO) table. Based on this model, we analyzed the multi-scale impact of two typical Chinese metropolises, namely Beijing and Shanghai, on global carbon emissions. Structural decomposition analysis and social network analysis (SNA) were used to explore the driving factors of consumption-based carbon (CBC) and the roles of metropolises in the carbon networks. Results showed that both Beijing and Shanghai are net embodied carbon consumers, which respectively drove 231.19 and 219.52 Mt global carbon emissions in 2017. These figures were underestimated by 12.54 % and 15.41 % when using the CMRIO. After China's economy entered a new normal, instead of technological progress, structural adjustment became the prominent factor driving the CBC reduction of metropolises. During 2012–2017, the consumption structure optimization reduced 18.87 and 32.48 Mt CBC in Beijing and Shanghai, respectively. Compared with other domestic regions, the CBC of Beijing has continued to increase, whereas that of Shanghai has declined. At the international scale, the combined net carbon emission imported by the two metropolises was 88.43 Mt in 2017, equivalent to 18.09 % of China's total carbon deficit. This indicates that metropolises have become pioneering regions for China to alleviate the carbon deficit in international trade. By using SNA, we further found that both metropolises are crucial carbon consumers in the global carbon network, with strong stability and obvious hub roles. Furthermore, various urban functions and geographical locations form the heterogeneous structural characteristics of CBC in the two metropolises, highlighting the need for different strategies for embodied carbon mitigation in these metropolises.

Consumption Structure Optimization Strategy for Scenic Spots Using the Deep Learning Model under Digital Economy.

The purpose is to find out the problems existing in the consumption economy structure of the scenic spots and to promote the rationalization of the consumption economy of the scenic spots. Based on the analysis of the applicability of the backpropagation neural network (BPNN) model, it uses BPNN to analyze the economic development level of Overseas Chinese Town East (OCT East). Firstly, the weight of each index is determined by the Analytic Hierarchy Process (AHP), and the expected value of the comprehensive evaluation is obtained. Secondly, to ensure the validity of the evaluation model for the development level of the tourism complex, the BPNN model is trained and tested to enable it to be applied to the evaluation of the economic development level of OCT East. The development level of OCT East from 2012 to 2021 is divided into three stages: high, higher, and lower. The development characteristics and existing problems of the OCT East are analyzed, and the optimization strategy of the consumption economy of the scenic spots is put forward in a targeted manner. The research results manifest, that from 2012 to 2021, the development level index of OCT East increased from 0.2457 to 0.5304, and it was in a state of steady growth. In 2019, the development level index reached 0.6497, and it was upgraded to “high-level,” but the average development level index of OCT East was only 0.5662, and there was a lot of room for improvement. According to the divided evaluation indicators, the development level of OCT East is evaluated. In 2012, the development level was low. From 2013 to 2018, it was at a high level, and from 2019 to 2021, it was a high level of development. By studying the Tourism Consumption Structure (TCS) of scenic spots in the OCT East, the research method of the consumption economic structure has been expanded. Therefore, it not only provides a reference for optimizing the consumption of scenic spots, but also contributes to the progress of the social tourism economy.

The average environmental efficiency technique and its application to Chinese provincial panel data.

In this study, we propose average environmental efficiency, a more comprehensive, fair, comparable, and robust environmental efficiency measurement considering all projection directions to the efficient frontier, and then it is used to evaluate the environmental efficiency of Chinese provinces from 2006 to 2017. Furthermore, we investigate the most influential factors of regional environmental efficiency via a feasible generalized least squares regression approach. The empirical results show that only nine Chinese provinces have average environmental efficiency greater than the national average, implying that two-thirds of the provinces still have much room for improvement. Additionally, environmental efficiency disparities exist between provinces and between four larger geographical areas. The east area achieved the best environmental efficiency over the studied period, better than the whole country, followed in order by the west area, central area, and northeast area. Moreover, we find that the energy consumption structure, government intervention, and economic openness significantly and negatively influence regional environmental efficiency. Finally, we provide policy implications in terms of energy consumption structure optimization, government supervision, and foreign investment introduction while considering the local conditions in different provinces.

Spatial-temporal evolution and driving forces of provincial carbon footprints in China: An integrated EE-MRIO and WA-SDA approach

The reduction of carbon footprint (CF) induced by cross-province trade is equally important for China with unbalanced economic development under the background of “double carbon” and “dual circulation” strategy. Based on the environmentally-extended multi-regional input-output (EE-MRIO) and weighted average structural decomposition analysis (WA-SDA) model, this study explored the distribution characteristics and spatial-temporal evolution process of three-perspective CFs (By province, Per capita, and Per ¥GDP) at the provincial level, and accurately revealed the socio-economic driving factors of provincial CFs from 2007 to 2017. Here we show that China's CF increased from 6151.4 Mt. in 2007 to 8778.6 Mt. in 2017, with an average annual growth rate of 3.6%. With the socioeconomic development, the gravity centers of CFs and per capita CFs were gradually shifting from eastern affluent provinces and populous municipalities to underdeveloped western China, while the provinces with relatively low carbon footprint intensities (CFIs) were still concentrated in affluent eastern coastal areas. It will be conductive to the overall improvement of national emission mitigation effective through introducing the advanced production technologies and scientific consumption awareness of developed provinces to eastern China in the future. Meanwhile, the corresponding control measures on CF reduction should pay attention to the changes of per capita consumption and population especially for economically-developed populous regions such as Shandong, Jiangsu, Guangdong, and Zhejiang. For Inner Mongolia, the bidirectional adjustment of emission intensity and production structure will be necessary for its CF reduction. Moreover, the continuous optimization of consumption structure will be expected to contribute more impacts on CF reduction under the demand-side reform. The obtained results and policy implications are expected to provide corresponding and holistic reference for decision-makers to develop more efficient carbon mitigation and management policies.

Carbon Emission Trading Policy and Carbon Emission Efficiency: An Empirical Analysis of China’s Prefecture-Level Cities

Improving carbon emission efficiency is an important means to achieve pollution reduction and sustainable economic development. Rather than focusing on the implementation of market-incentive environmental policies in developed countries, we study the effect of the implementation of market-incentive environmental policies on the efficiency of carbon emissions in developing countries, which is generally ignored by frontiers researches. Based on panel data of 282 cities at prefecture-level and above in China from 2007 to 2017, we first adopt the non-radial distance function (NDDF) and global DEA model to measure the carbon emission efficiency of China’s cities. Then we take the Chinese carbon emission trading pilot as a quasi-natural experiment and explore the impact of carbon emission trading policy on carbon emission efficiency based on DID method. And the mechanisms are analyzed through the mediation effect model. It is found that the carbon emission rights trading policy can significantly improve the carbon emission efficiency of the pilot cities, and it mainly plays a role through three channels: technological progress effect, green innovation effect and energy consumption structure optimization effect. The heterogeneity test results show that for resource-based cities and cities with a higher degree of marketization, the carbon emission trading policy has a more obvious effect on improving carbon emission efficiency.

Analysis and forecast of China's energy consumption structure

In the context of the practice of high-quality social development gradually deepening, the optimization of energy structure is an important link to promote high-quality economic development. We used China's historical data from 1980 to 2019, and identified 17 influencing factors of its energy consumption structure. From four dimensions (economy, structure, technology, population and policy), Copula function was employed to establish a multi-factor dynamic support vector machine model to predict the advanced index of energy consumption structure in 2020–2030. The results show that (a) China's energy consumption structure is being optimized. An up-trend is found in the advanced index of China's energy consumption structure, and the proportion of its coal consumption shows a downward trend, but the decline is gradually decreasing. (b) Energy price adjustment, increased rural income, industry structure improvement, higher R&D expenses contribute to energy consumption structure optimization in China. (c) China is able to meet the carbon emission target set for 2030 on schedule. China is expected to reach carbon emission peak in 2030, and non-fossil energy will account for about 21% in 2026. The carbon emission target per unit of GDP is expected to be completed ahead of schedule.

Energy carbon emission structure and reduction potential focused on the supply-side and demand-side.

In recent years, the environmental problems caused by excessive carbon emissions from energy sources have become increasingly serious, which not only aggravates the climate change caused by the greenhouse effect but also seriously restricts the sustainable development of Chinese economy. An attempt is made in this paper to use energy consumption method and input-output method to study the carbon emission structure of China's energy system and industry in 2015 from two perspectives, namely China's energy supply side and energy demand side, by taking into account the two factors of energy invest in gross capital formation and export. The results show that neglecting these two factors will lead to underestimation of intermediate use carbon emissions and overestimation of final use carbon emissions. On energy supply side, the carbon emission structure of China's energy system is still dominated by high-carbon energy (raw coal, coke, diesel, and fuel oil, etc.), accounting for more than 70% of total energy carbon emissions; on the contrary, the natural gas such as clean energy accounts for only 3.45% of total energy carbon emissions, indicating that the energy consumption structure optimization and emission reduction gap of China's energy supply side are still substantial. On energy demand side, the final use (direct consumption by residents and government) produces less carbon emissions, while the intermediate use (production by enterprises) produces more than 90% of the total energy carbon emissions. Fossil energy, power sector, heavy industry, chemical industry, and transportation belong to industries with larger carbon emissions and lower carbon emission efficiency, while agriculture, construction, light industry, and service belong to industries with fewer carbon emissions and higher carbon emission efficiency. This means that the optimization of industrial structure is conducive to slowing down the growth of energy carbon emissions on the demand side.

Energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture: An input–output perspective

The greenhouse effect caused by excessive carbon emissions seriously threatens the sustainability of the global economy. Although the secondary and tertiary industries are the principal contributors to CO2 emissions’ growth, the continuous and stable development of agriculture also intensifies carbon emissions to a certain extent. Based on this, this paper uses the input–output method, an energy consumption model, and structural decomposition analysis to study energy-related CO2 emissions and structural emissions’ reduction in China’s agriculture from 2007 to 2017. The research results show that (1) In 2007–2017, the input structure effect and the energy intensity effect inhibited the growth of energy-related CO2 emissions in China’s agriculture, with the reduction effect of energy intensity effect being the more prominent, whereas the final demand effect and the energy structure effect contributed to promoting China’s agricultural energy-related CO2 emissions’ growth, with the final demand effect being the greater promoting factor. (2) The energy structure effect failed to reduce energy-related CO2 emissions in China’s agriculture because the optimization of consumption structure is still not obvious on China’s agricultural supply-side. The share of energy consumption from high-carbon fuels such as raw coal, coke, and other washed coal has increased, rather than decreased, and the optimization of low-carbon energy sources such as natural gas is still slow, thus limiting the emissions’ reduction effect in the energy structure. (3) The reason why the final demand effect has promoted the growth of energy-related CO2 emissions in China’s agriculture is the expanding demand for agriculture in the entire industrial system. Specific to each industry, the secondary industry has played a major role in the final demand effect, accounting for more than 50% of the final demand effect, followed by the primary and the tertiary industries. Finally, this paper provides a theoretical and practical basis for accurately and efficiently implementing emissions’ reduction from the perspectives of the industrial system on the demand-side and the energy system on the supply-side and also in China’s agriculture.

A bi-level multi-objective linear fractional programming for water consumption structure optimization based on water shortage risk

Abstract In this study, a framework of a bi-level multi-objective linear fractional programming (BMLFP) approach is developed for the optimization of water consumption structure based on water shortage risk. It incorporates linear fractional programming (LFP), multi-objective programming (MOP) into bi-level programming (BP). The model considers the water shortage risk measured by interior-outer-set risk assessment method, and integrates it into inexact water resources optimization model. The complicated model not only can enhance the conventional programming method through the interactive influence and mutual restriction between the upper- and lower-level decision processes, and multiple objectives, but also improve the robustness of conventional programming methods by integrating the water shortage risk. Besides, the model could achieve the tradeoff between equality and economic benefit of system and figure out the interaction amid water shortage risk, water allocation and objectives. It is applied to a case study to conduct water resources management among different water users including agricultural, industrial, domestic, and ecological sectors in the middle reaches of Heihe River Basin, northwest China. The water allocation schemes with ten water-shortage risk scenarios is formed to compare the influence of water shortage degree on water allocation and figure out the optimal water allocation schemes. Besides, the performance of the developed model is enhanced by comparing with the two-level linear fractional water management (TLFWM) model and actual condition. The results indicate that the water shortage belongs to general risk area, and the water-shortage risk has obvious effect on agricultural water allocations while has insensitive influence on that of the industrial, domestic and ecological sectors. Compared with the TLFWM model and actual condition, the developed BMLFP model could improve the equality and benefit of system and lessen the water allocation. The decision makers can find better water-allocation decision alternatives obtained from BMLFP model according to their risk attitude to support social harmony and economic development.

Energy saving and emission reduction of fossil energy based on low carbon economy and its consumption structure optimization

Abstract Energy saving and emission reduction have been not only a slogan but also a policy in this modern society where the phenomenon of greenhouse is exacerbated. In this study, calculation method of carbon emission and integrated parallel acquisition technique (IPAT) scenario prediction model were combined to predict the changes of total carbon emissions, energy structure distribution, and carbon emission intensity under three measures of energy saving and emission reduction in the next ten years in Shandong, China. The results showed that the total carbon emission increased year by year, and the coal ratio and carbon emission intensity decreased under the natural scenario; the total carbon emission in the weakly constrained scenario would increase annually until 2029, the amplitude was smaller than that of the natural scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than that of the natural scenario. Under the strongly constrained scenario, the total carbon emission would increase annually before 2025, and the amplitude was smaller than the weakly constrained scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than the weakly constrained scenario.

Multi-Scenario Analysis of Energy Consumption and Carbon Emissions: The Case of Hebei Province in China

In view of the complexity of the energy system and its complex relationship with socio-economic factors, this study adopts the Long-range Energy Alternative Planning (LEAP) model, a technology-based, bottom-up approach, scenario-based analysis, to develop a systematic analysis of the current and future energy consumption, supply and associated Green House Gas (GHG) emissions from 2015 to 2050. The impact of various energy policies on the energy system in Hebei Province was analysed by considering four scenarios: a Reference Scenario (REF), Industrial Structure Optimization Scenario (ISO), Terminal Consumption Structure Optimization Scenario (TOS) and Low-carbon Development Scenario (LCD). By designing strategic policies from the perspective of industrial adjustment, aggressive energy structure policies and measures, such as the ISO and the TOS, and even more aggressive options, such as the LCD, where the percentage of cleaner alternative energy sources has been further increased, it has been indicated that energy consumption will have increased from 321.618 million tonnes of coal equivalent (Mtce) in 2015 to 784.88 Mtce in 2050 in the REF, with a corresponding increase in GHG emissions from 920.56 million metric tonnes (Mt) to 2262.81 Mt. In contrast, the more aggressive policies and strategies involved in the LCD, which combines the ISO with the policy-oriented TOS, can lower energy consumption by 50.82% and CO2 emissions by 64.26%. The results shed light on whether and how these scenarios can shape the energy-carbon emission reduction trajectories and develop the low-carbon pathways in Hebei Province.

Study on the Effect of Energy Consumption Structure on Carbon Intensity of Hebei Province from the Perspective of Sensitivity

Based on the Kaya identity, the regression equation, involving the carbon intensity, energy consumption structure and energy intensity, is built, then the direct and indirect effect of the energy consumption structure on the carbon intensity is revealed, with the path analysis. Result shows that the shares of coal consumption and primary power are the major driving factors on the reduction of carbon intensity, while the oil consumption ratio, natural gas consumption ratio and energy intensity are the main inhibiting factors of carbon intensity decline. Furthermore, with the carbon intensity sensitivity based on Cobb-Douglas production function and logarithm production function, the carbon emission reduction potential of energy consumption structure optimization is assessed. Result shows that a reduction of one percentage point in the share of coal consumption is substituted completely by the share of oil, gas, primary power consumption, respectively, and the carbon intensity would be reduced by 0.3981 percentage points, 0.4755 percentage points, 0.7476 percentage points, respectively. The reduction of the share of coal consumption will only reduce the carbon intensity by 3.61 percentage points.

The achievement of the carbon emissions peak in China: The role of energy consumption structure optimization

Optimizing the coal-dominated structure of energy consumption would effectively contribute toward ensuring that China's carbon emissions peak by 2030. To investigate whether this goal can be achieved through such optimization, the present study proposed a new economic-carbon emission-costs (ECC) multi-objective optimization model. Our findings show that the carbon emission peak will be achieved between 2025 and 2028, most likely in 2028, with 10.12 billion tonnes CO2. The peak of coal consumption was achieved in 2013 whereas the oil consumption cannot be peaked before 2035 in the GDP preference. In the process, China's GDP will be able to maintain an average annual growth rate of 5.9%–6.3% between 2017 and 2030, while the carbon emission average annual growth will be 0.5%–1.1%. The carbon intensity in 2030 can drop to 71.9% based on the 2005 level, which would exceed the committed reduction goal of carbon intensity equal to 60–65%. Furthermore, we find that the volume and time of the CO2 emission peak are not sensitive to renewable electricity price, but are sensitive to the assumptions both the minimum average annual growth rates of GDP and the average annual reduction rate of energy intensity.