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Electric power is the basic industry of a modern country. The rapid development of the power industry has promoted economic development and social progress. With the establishment of the carbon neutrality target of carbon peak, China's power industry is also facing new situations and new challenges. This paper innovatively introduces the concept of allometric growth in biology, and uses the data of provincial electricity consumption and economic development from 1972 to 2017, to study the allometric growth relationship between electricity consumption and economics in China at multiple scales. Combined with the spatial autocorrelation analysis method, the temporal and spatial characteristics of allometric growth between electricity consumption and economic development were researched based on the analysis of spatial correlation of GDP electricity consumption intensity. The results show that: (1) The provincial GDP electricity consumption intensity shows an aggregate distribution, and the local aggregate pattern changed significantly from 1972 to 2017. (2) The High-High Cluster gradually disappeared, and the Low-Low Cluster moved southward, appearing in the middle and lower reaches of the Yangtze River. (3) The differential growth coefficient of electricity consumption and economic development increased initially and decreased from 1972 to 2017. (4) GDP would increase by 7.4%, 20.1%, and 15.6% in the simulation of electricity consumption, increasing 10% at the three stages of China's economic development. (5) The allometric growth coefficients of electricity consumption and economic development are very different in the three economic belts, eight economic zones, and provinces with different characteristics at the different stages of economic development. Formulating differentiated regulation policies for regional economic development and electricity consumption will be conducive to the coordinated development of electric power production and the national economy. [ABSTRACT FROM AUTHOR]

Based on the background of carbon neutrality targets, panel data of 30 Chinese provinces during 2000–2019 were selected in this paper to explore the nonlinear correlation mechanism among transportation, tourism development, renewable and non-renewable energy consumption and economic growth. Firstly, by employing a fixed effects model as the base model, we confirmed the existence of correlations among these variables. And the nonlinear moderating effects were also verified by setting interaction terms in the model. Secondly, we further discussed the characteristics of the nonlinear relationship among the variables by employing a threshold effects model. Our results demonstrated that the effect of transportation on economic growth is positive and significant if and only if renewable energy consumption surpasses a certain threshold. Meanwhile, we also found that the effect of tourism development on economic growth is positive when non-renewable energy consumption is under a certain threshold. Finally, our study indicated that transportation maintains a positive and significant effect on economic growth within any threshold of tourism development, and the positive effect gradually increases as the threshold rises. The findings of this paper suggested that for China, the contribution of transportation and tourism to economic growth can be expanded by further expanding the substitution of renewable energy for non-renewable energy. [ABSTRACT FROM AUTHOR]

One of the secrets for China's economic growth miracle is continuous investment in infrastructure, and the role of power infrastructure is irreplaceable. However, previous studies mostly focused on generation-side indicators rather than grid-side indicators, ignoring the importance of the mega-grid established in China. As a result, this paper is committed to exploring the economic growth pulling effect of power grids with different voltage levels in the nonlinear framework. On the premise of determining the robustness of estimation parameters by instrumental variable method, the panel smooth transition regression (PSTR) model is used to capture panel heterogeneity. To be exact, the regional economic growth driven by extra-high voltage (EHV) and ultra-high voltage (UHV) power grids presents a regime transition accompanied by the growth of regional power generation and electricity consumption per capita. This means that the mega-grid is more conducive to the economic growth of energy bases and load centers. To further build and improve the state-leveled mega-grid into a nationwide basic platform for trans-regional renewable energy transmission and distribution is a key measure to enhance China's sustainable economic growth under the constraints of carbon peak and carbon neutrality targets. [ABSTRACT FROM AUTHOR]

Financial development (FD) is pioneered to solve the problem of vital capital supporting for carbon neutrality. Detecting the effect of FD on carbon emissions (CE) plays an important role in accurately planning FD, and guiding carbon neutrality in China. To clarify the influence of FD on CE and the underlying pathways concerning scale effect, structural effect, and technological effect, this paper use China’s 285 city-level data spanning 2003–2016 and fixed effect (FE) models and instrumental variable (IV) methods to test the effect of FD on carbon neutrality in China. The findings show that (1) the development of financial deepening, financial interrelation ratio, and financial intermediary all prompt carbon emissions at the national level. (2) Regional heterogeneity indicates that FD benefits carbon neutrality in mid-Western cities but fails in eastern cities. (3) FD increases carbon emissions by boosting electricity consumption but decreases carbon emissions by promoting structural optimization and technological progress. (4) Environmental regulations partially curb the carbon emissions effect of FD. This paper proposes some suggestions to reduce CE and gain carbon neutrality in China by greening FD, optimizing industrial structure, promoting technologi)cal innovation and strengthening environmental regulation. [ABSTRACT FROM AUTHOR]

The clean and low-carbon transition of China's power sector is of great importance to the achievement of dual carbon targets and the control of global warming. This paper first estimates the remaining carbon budget of the power sector under a 1.5 °C temperature control target and on this basis constructs 1.5 °C and 2 °C power transition scenarios, examining key boundary conditions such as economic development and changes in the cost of power generation technologies. Second, the Genetic Algorithm-Extreme Learning Machine (GA-ELM) model is used to forecast the electricity demand for the next forty years. Finally, with the objective of minimising the total planning cost, a pathway optimisation model of the power system is constructed to explore the optimal transition path for the power system using the dual carbon target, carbon budget and electricity demand as the main constraints. The results of the study show that the carbon budget of the Chinese power sector is approximately 7.1 × 1010 t CO2 for a 1.5 °C temperature control target. The electricity demand tends to saturate after 2050 and reaches 1.58 × 1013 kWh in 2060. The time of the carbon peak and carbon neutralisation in the power sector is 5 years ahead of the double carbon target. By 2060, the power system will be dominated by new energy sources, with the proportion of installed non-fossil energy capacity at over 90% and the proportion of non-fossil energy generation at over 85%. Compared to that under the 2 °C temperature control target, the power sector under the 1.5 °C temperature control target needs to accelerate the pace of the low-carbon transition of electricity and deal with key issues such as the orderly withdrawal of coal power, the construction of a diversified clean energy system and the application of carbon capture devices. This study recommends that the process of building a zero-emissions power sector requires a good pace of the construction of new power systems at a suitable pace, increased efforts to tackle key technologies and improved relevant market mechanisms. China's carbon–neutral pathway in the power sector also has implications for other countries' clean, low-carbon transitions of their power systems. [ABSTRACT FROM AUTHOR]

The petrochemical industry is traditionally a high energy consumption industry, thus energy saving in this industry is of great significance in terms of trying to achieve the national goal of "carbon-peak and carbon neutrality" in China. Diesel is one of the main products of petrochemical enterprises. The energy consumption of a diesel hydrotreating (HDT) unit is relatively high due to the high reaction temperature and pressure of HDT reactors. In order to further explore the energy consumption of diesel HDT units under different operating conditions, this paper establishes a full-process simulation based on the kinetics of hydrodesulfurization, hydrodenitrogenation, and aromatics saturation, in Aspen Plus to discuss the effect of different operating conditions on the electricity, steam, and fuel gas consumptions. The results show that with the increase of reaction temperature or reaction pressure, the sulfur content in diesel oil decreases, but the consumption of related utilities and comprehensive energy consumption increases. In addition, this paper also analyzes the energy consumption changes under the operating condition boundaries when the diesel quality is unchanged. When the reaction pressure is 8 MPa and the reaction temperature is 317.5 °C, the comprehensive energy consumption is 5% lower than the energy consumption with the reaction pressure of 6 MPa and the temperature of 320 °C. Therefore, on the premise of satisfying the diesel product quality, the comprehensive energy consumption of the diesel HDT unit can be reduced by increasing the reaction pressure and decreasing the reaction temperature. [ABSTRACT FROM AUTHOR]

The achievement of the carbon peaking and carbon neutrality targets requires the adjustment of the energy structure, in which the dual-carbon progress of the power industry will directly affect the realization process of the goal. In such terms, an accurate demand forecast is imperative for the government and enterprises' decision makers to develop an optimal strategy for electric energy planning work in advance. According to the data of the whole social electricity consumption in Jiangsu Province of China from 2015 to 2019, this paper uses the improved particle swarm optimization algorithm to calculate the fractional-order r of the FGM (1, 1) model and establishes a metabolic FGM (1, 1) model to predict the whole social electricity consumption in Jiangsu Province of China from 2020 to 2023. The results show that in the next few years the whole social electricity consumption in Jiangsu Province will show a growth trend, but the growth rate will slow down generally. It can be seen that the prediction accuracy of the metabolic FGM (1, 1) model is higher than that of the GM (1, 1) and FGM (1, 1) models. In addition, the paper analyzes the reasons for the changes in the whole society electricity consumption in Jiangsu Province of China and provides support for government decision making. [ABSTRACT FROM AUTHOR]

To reflect the future electricity demand variations in the industrial sector of western China under the "carbon peaking" and "carbon neutral" strategies, that the traditional methods of electricity consumption forecasting are no longer effective, this paper proposes a new hierarchy of electricity demand influencing factors based on the transition path of low-carbon and CO 2 emission intensity constraints of the industrial sector. Then, the feedback equations of the influencing factors based on CO 2 emission intensity is constructed, and a long-term electricity forecast model based on system dynamics is established. Finally, taking Ningxia Province as an example, the electricity demand and carbon emissions of the industrial sector are predicted under different policy constraint scenarios. The results show: (1) In the baseline scenario, electricity demand will tend to saturate with about 140.1TWh in 2030, and the CO 2 emission level of electricity demand will peak at 72.52 million tons in 2029. (2) Compared with the system dynamics model without considering CO 2 emission intensity, the mean absolute percentage error is reduced about 3.16 % by inputting the influence factors and regression coefficients designed in this paper. (3) Compared with other forecasting methods, the model proposed in this paper has the lowest mean absolute percentage error of 2.75 %. The model improves the accuracy and provides a reference for planning departments to develop energy transition plans, where the prediction results reflect the reality of the industrial sector in western China. Correspondingly, this model has reference value for relevant institutions in forecasting the electricity demand under the low-carbon development path in industrial sectors of different regions. • Influencing factors are screened according to the low-carbon transition paths. • The distributed power installed capacity and load interaction coefficient are considered. • Multiple regression equations are developed between CO 2 emission targets and influencing factors. • A long-term electricity demand forecast model driven by CO 2 constraints is constructed. • The multi-scenario electricity demand in western China is forecasted in response to carbon peaking and carbon neutral targets. [ABSTRACT FROM AUTHOR]

To achieve China's carbon peak and carbon neutrality goals, conducting systematic research on the energy, economy, and emission factors that affect the sustainable development of society is of great significance. This paper first uses the vector error correction model (VECM)-based Granger causality test to analyze the joint causal relations and feedback correction mechanisms among energy consumption, economic growth, and CO2 emissions in China from 1980 to 2019 at the energy heterogeneity level; then, analyzes the decoupling effect of China's four major energy sources (coal, oil, natural gas, and electricity) and economic growth from the perspective of energy heterogeneity; finally, the Tapio decoupling elastic model is decomposed into the emission reduction elasticity and energy saving elasticity to analyze the decoupling causality chain of the economy, energy, and carbon emissions. The research results show that there is a long-term, two-way causal relation between coal consumption and CO2; coal consumption has a one-way causal relation with economic growth; and long-term, two-way causal relations exist between oil and CO2, natural gas and CO2, electricity and CO2, electricity, and economic growth. In addition, when energy consumption, economic growth, and CO2 emissions deviate from their equilibrium states in the short term, various energy consumption, economic growth, and CO2 emissions in the previous year will be adjusted by 19.5%, 0.6%, ..., 7.7%, and 3.4% to bring the nonequilibrium states back to the long-term equilibrium states. Furthermore, the energy-saving elasticity of China's total energy, coal, oil, and natural gas is the main factor affecting the corresponding decoupling elasticity, but the emission reduction elasticity of electricity has a stronger impact on the decoupling elasticity than the emission reduction elasticity does. [ABSTRACT FROM AUTHOR]

The fundamental way for China to achieve carbon peak and carbon neutrality is to continuously improve the penetration of renewable energy (RE) in the power system and establish an electricity mix dominated by RE power. According to China's 14th Five-Year Plan, the proportion of non-fossil energy power generation should reach about 39% by 2025. However, the impact of the increase in share of RE power on carbon emissions of the electricity sector remains to be estimated. To this end, the purpose of this paper is to capture the nonlinear characteristics of RE on electricity-related carbon emissions by using panel smooth transition regression (PSTR) model and provincial panel data covering the period 1991–2020 under the background of provincial RE quota system. The estimation results indicate that, (i) in the four transition variables of PSTR model such as the installed capacity of power generation, substation equipment capacity, total electricity consumption and total power generation, the nexus between RE deployment and electricity-related carbon emissions was detected as nonlinear. (ii) The heterogeneous RE carbon abatement effect is reflected in its increasing penetration in provincial power systems. (iii) Carbon abatement performance of RE generation decreases in the negative range with the increase of transition variables. (iv) The estimation coefficient of RE is positive at the higher value of the transition variable in the PSTR model of carbon emissions per capita. As a result, the RE quota with the best emission abatement performance should be designed based on the provincial power resource endowment and the medium and long-term expected electricity consumption. [ABSTRACT FROM AUTHOR]

• City level data in China was used to examines the effects and mechanisms of digital finance on residential carbon emissions. • In terms of the structural composition of carbon emissions, digital finance mainly increases residential carbon emissions by affecting electricity consumption and transportation in China. • Digital finance can increase residential carbon emissions through the consumption effect and employment effect. Reducing residential carbon emissions is important for all countries to achieve carbon neutrality. For such task, the impact of digital development of finance on residential carbon emissions cannot be ignored. This paper estimates residential carbon emissions in 186 Chinese cities from 2011 to 2019 and examines the effects and mechanisms of digital finance on residential carbon emissions. The results show that digital finance exacerbates residential carbon emissions in China. In terms of the structural composition of carbon emissions, digital finance mainly increases residential carbon emissions by affecting electricity consumption and transportation in China. Mechanism analysis shows that digital finance can increase residential carbon emissions through the consumption effect and employment effect. [ABSTRACT FROM AUTHOR]

Considering China's goal of carbon neutrality, understanding the influence path and dynamic correlation between electricity consumption and relevant factors is essential for formulating a reasonable electricity development strategy and promoting high‐quality socio‐economic development. Based on panel data of 30 provinces in China from 2000 to 2019, this study adopts the panel VAR model for research, with four notable findings. (1) Economic development, urbanization, and the urban–rural income gap directly affect electricity consumption. Industrial structure upgrading can affect electricity consumption through economic development and urbanization. Electricity consumption only directly affects economic development, whereas the effect on the other three variables could be transmitted through economic development. (2) Electricity consumption first inhibits and then promotes industrial structure upgrading, and it inhibits the urban–rural income gap. (3) Industrial structure upgrading first promotes and then inhibits electricity consumption. The urban–rural income gap inhibits electricity consumption. (4) Industrial structure upgrading and the widening income gap might hinder economic development through the adverse effect on electricity consumption. Based on these findings, several policy suggestions are proposed. [ABSTRACT FROM AUTHOR]

Implementing real-time carbon emissions monitoring at the enterprise level enables the detailed breakdown of carbon neutrality goals for microcosmic enterprises, which is of paramount significance in ensuring the precision of policy formulations. Grounded in China's historical electricity consumption and carbon emissions data, this study utilizes the network approach and input–output methods to compute and predict direct and indirect transmission coefficients of electricity consumption and carbon emissions in each province. We establish a methodology that enables the monitoring of real-time carbon emissions of enterprises based on corporate electricity consumption data. Using the metal smelting industry in Guangxi as an example, our findings are as follows: First, in 2020, the comprehensive carbon emissions of the ferrous metal smelting industry in Guangxi reached 58.84 million tons, marking a notable increase of 42.78% compared to emissions in 2014, indicating that emissions reductions are imperative. Second, significant regional variations in emission coefficients are observed, with values ranging from 14 g CO2/KWh to 940 g CO2/KWh among provinces. Meanwhile, the trends of direct carbon emissions and indirect carbon emissions are totally different, underscoring the importance of comprehensive carbon accounting in informing policy decisions. Third, through the carbon emissions real-time monitoring of 75 metal smelting industry enterprises using electricity big data, we identified that the distribution of emissions across industries, time periods, and regions is uneven. Overall, this method can optimize carbon emission measurement techniques to a higher spatiotemporal resolution and more microscopic monitoring subjects, providing essential numerical foundations for promoting carbon emissions reduction and sustainable development. [ABSTRACT FROM AUTHOR]

Accelerating the energy transition of rural households in mountainous areas is an important step to improve energy poverty and mountain ecosystems and to help achieve carbon peaking and carbon neutrality. This paper explores the transformation of rural cooking energy in mountainous areas based on field investigation data from six villages in Chongqing. It systematically reveals the transformation process, driving factors and carbon emission changes in household cooking energy consumption in mountainous areas in the past 20 years by using mathematical statistics, multinomial logit models and the emission coefficient method. The results are as follows. (1) There has been a significant transition in the consumption of rural household cooking energy in mountainous areas. There has been a significant change in the proportion of different energy sources, including fuelwood (59.2%–1.9%), coal (16.5%–0%), gas (12.6%–15.5%), natural gas (6.8%–51.5%) and electricity (4.9%–31.1%). (2) The main driving factors of the household cooking energy transition in mountainous areas are income per capita, householder age, and householder education level. (3) The average annual household carbon emissions from rural household cooking energy decreased remarkably. The average annual household carbon emissions decreased from 6.80 t to 2.62 t in fuelwood (5.40–0.91 t), electricity (0.19–1.28 t) and natural gas (0.03–0.25 t) emissions during the study period. The contribution of this study is to investigate how the household cooking energy transition contributes to carbon peaking and carbon neutrality from a micro perspective. At the same time, this paper explores the mechanisms between the cooking energy transition and the ecological dividend of urbanization and proposes future reform initiatives for rural energy in mountainous areas to help achieve the goals of carbon peaking and carbon neutrality. [ABSTRACT FROM AUTHOR]

The development of human society has led to the growing consumption of industrial products, which generates significant amounts of carbon emissions. However, relatively few in-depth studies have been conducted on the influence of different demand factors (e.g., household consumption, government consumption, export, and capital formation) on carbon emissions, which hinders the development of targeted industrial policies. To address this issue, an analytical framework based on input–output theory, the hypothesis extraction method, and complex network analysis was established to estimate the intrinsic influence of different demand factors on the embodied carbon transfer between provinces in China. The key findings can be summed up as follows: (1) The macro direction of China's embodied carbon transfer runs from resource-rich northern provinces to industrially developed southern provinces. (2) From the perspective of different demand factors, capital formation is the most significant contributor to China's embodied carbon transfer, with the construction industry being the most important driver. In contrast, government consumption causes the least embodied carbon transfer, but it has the highest average carbon emission intensity. (3) According to complex network theory, the carbon transfer networks via provinces and industries caused by exports are the most concentrated, with the manufacture of electrical machinery and electronic equipment serving as the main source of demand. In contrast, the carbon transfer network resulting from household consumption exhibits a high level of decentralization, with dominant sectors including electric power, gas and water production, and supply and other services. Based on these findings, this study is expected to contribute targeted suggestions with which provinces and industries can formulate demand-side carbon reduction policies for different demand factors, which will contribute to the achievement of "carbon peaking and carbon neutrality". [ABSTRACT FROM AUTHOR]

In the context of carbon neutrality, renewable energy, especially wind power, solar PV and hydropower, will become the most important power sources in the future low-carbon power system. Since wind power and solar PV are specifically intermittent and space-heterogeneity, an assessment of renewable energy potential considering the variability of wind power and solar PV with high temporal resolution in different regions will facilitate more accurate identification of the decarbonization pathway of power system. In this paper, the complementary output potential of wind-solar-hydro power every 15 min in 31 Chinese provinces is evaluated by developing a multi-objective optimization model based on Nondominated Sorting Genetic Algorithm II. The results show that the total annual complementary power generation potential in China is 17.57 PWh, of which the three components account for 47.8%, 25.3% and 26.9%, respectively. Inner Mongolia and Tibet are the major renewable energy source provinces with annual complementary power generation potential of 5330 TWh and 3907 TWh, accounting for 30.3% and 13.7% of the total power generation potential, respectively. The temporal potential of wind-solar-hydro power varies greatly, with daily potential is more volatile than monthly. Seasonal and spatial heterogeneity of the complemental renewable potential makes some provinces suffer power shortage during long-hours period especially in winter. Our findings will encourage a higher penetration of renewable energy, the promotion of multi-energy complementarity, and the development of inter-provincial power transmission and energy storage infrastructure in China's future power sector. • A multi-objective wind-solar-hydro complementary optimization model is developed. • Electricity supply and demand have gaps at different temporal and spatial scales. • Wind-solar-hydro complementary potential shows great temporal and spatial variation. • Renewable complementarity can improve China's future power system stability. [ABSTRACT FROM AUTHOR]

Business parks account for 30% of China's total carbon emissions. Exploring emissions reduction approaches for business parks is crucial to achieve a net-zero emissions target, as well as for achieving a representative example for all types of emissions entities. Business parks mainly adopt two types of emissions reduction approaches: energy-saving renovations and purchasing carbon reduction products. However, there are limited studies focusing on the optimal combinations of the two approaches for reaching net-zero emissions and evaluating the cost effectiveness. To find a feasible and quantified way to build net-zero business park, a comprehensive path decision model is proposed. The problem is broken down into two parts: the optimal carbon reduction portfolio and the optimal electricity saving were researched. For the optimal product portfolio, the Markowitz theory is employed to balance the risk of carbon reduction products with the expected cost. In the part of optimal electricity saving, considering a ten-year life cycle, the total cost includes renovation investment, carbon reduction products cost, and cost saving of electricity consumption reduction. Based on the energy consumption, technical, and price data, the combination of energy-saving renovations and carbon reduction products is optimized. The model suggests a business park can save 24% of energy consumption through renovation investment and purchase CCER as 66% of the carbon reduction product portfolio. Taking only purchasing carbon reduction products as a benchmark to assess economic efficiency, implementing an optimized level of energy-saving renovation is found to save 16% of the comprehensive cost for the life cycle required to achieve zero carbon emissions. This model provides a new comprehensive optimization idea that will help future parks make decisions to achieve zero-carbon emission targets. [ABSTRACT FROM AUTHOR]

As China's largest source of carbon emissions, the power sector's transition to lower levels of carbon emission intensity is critical to achieving China's carbon emission (CE) reduction commitments. In this paper, the CE intensity of electricity generation (CEIE) in 30 provinces from 2000 to 2019 is calculated, and the temporal evolution and spatial distribution characteristics of CEIE are discussed. Then, the spatial spillover effect of each driving factor on CEIE is calculated using the spatial Durbin model. The CEIE reveals a first increasing and then decreasing trend, and the regional differences gradually decrease. Simultaneously, the CEIE has a significant positive spatial correlation, and the overall spatial distribution pattern is high in the northeast and low in the southwest of the country. Economic development and foreign trade both inhibit CEIE, while electricity consumption, research and development, unit operation level, and power structure all promote CEIE. Each driving factor's influence on regional CEIE exerts a spatial spillover effect. Among them, the spatial positive spillover effects of industrial structure and environmental protection investment are the most obvious, while the spatial spillover effects of other driving factors are not apparent. Furthermore, these findings provide practical insights into policy adjustments and the coordinated interregional development of low-carbon electricity. [ABSTRACT FROM AUTHOR]

As an important path to realizing carbon neutrality in China, digital development may also lead to the growth and rebound of energy demand. Investigation of digital rebound effect is important to evaluate whether digital development can effectively conserve energy. Thus, this paper examines the impact of digital development on electricity consumption and its heterogeneity among Chinese cities, and further evaluates the sizes of energy rebound effect triggered by digital development. The results show that, first, electricity consumption in Chinese 285 cities increases by 7%–20% due to digital development, and this positive influence tends to increase with the increase in electricity consumption. Second, in the eastern, high-income, and high-tertiary industry share regions, digital development has a greater role in promoting electricity consumption. Third, the digital development of Chinese cities induces the rebound effect of electricity consumption, and even induces the backfire effect in the early stages of digital development. Finally, the energy rebound effect tended to decrease over time, with a partial rebound in electricity consumption, and the minimum rebound effect was 22%. The results highlight the necessary attention on digital rebound effect, and the importance of low-carbon digital development. • Electricity consumption increased 7%–20% due to digital development in China • The positive influence tends to increase with the increase in electricity consumption • Digital development in Chinese cities leads to energy rebound effect • The energy rebound effect tended to decrease over time, with a partial rebound [ABSTRACT FROM AUTHOR]

To fulfill the goals of sustainable development and emission reduction, China has established the consumption side renewable portfolio standards (RPS) and domestic nationwide carbon emission trading (CET) scheme. However, there are intricate interrelationships between the two mechanisms as they overlap in policy goals and implementation measures, which might lead to policy synergy or policy ineffectiveness. Therefore, investigating the coupling effects in the context of policy coexistence is imperative for policy optimization and realization of the policy objectives. In this study, a simulation model cooperating the electricity market, the excess consumption trading market, the tradeable green certificates (TGC) market and the CET market, based on the system dynamics (SD) methodology is proposed. The paper comprehensively investigated the complex relationship between the four markets and conducted experiments with different scenario settings in RPS quota goal and carbon quota goal. The results illustrate that: (1) Both mechanisms can facilitate the expansion of renewable electricity and improve the power supply structure. (2) The quota goals of the two policies should be reasonably set as they contain both the abovementioned overlapping effects, as well as conflicts in the effects on the total electricity supply and electricity price. (3) The consumption side RPS is conductive for market absorption of renewable electricity with a proper higher RPS quota goal. (4) The TGC market is oversupplied, and the price keep decreasing under the consumption side RPS, more demand incentive mechanisms are required for TGC market. • Implementation mechanism and effects of the consumption side RPS are investigated. • Policy coordination between the consumption side RPS and CET in China are examined. • The effects of consumption side RPS and CET have both overlaps and conflicts. • Ratio of renewable electricity consumption is positively correlated with RPS quota goal. [ABSTRACT FROM AUTHOR]

As the world's largest developing country, China has set the target of "reaching carbon peak by 2030 and carbon neutrality by 2060". China's power sector faces many challenges in reducing carbon emissions. This article forecasts electricity consumption from 2021 to 2060 by selecting five influential elements on power demand. Furthermore, the system dynamics model simulates the carbon emission system on the supply and demand side of the power industry. This paper innovatively incorporates the technology factor of carbon capture, utilization, and storage (CCUS) into the supply-side model. In this study, the process of carbon neutrality is separated into three stages, including carbon peak stage (2020–2030), deep low-carbon stage (2030–2050) and zero-carbon stage (2050–2060). The factors of energy storage technology and CCUS technology make significant contributions in the latter two stages. Based on the different development levels of the variables, three scenarios are set as business-as-usual scenario (BAU), low carbon scenario(LC) and ultra-low carbon scenario (ULC). Then scenario analysis is employed to simulate the carbon emission in the future. The scheme under the ULC scenario is the optimal solution to achieve carbon neutrality. Improving flexible transition of thermal power units and promoting the growth of renewable energy are China's primary strategies of achieving carbon peak and carbon neutrality. • System dynamics model is applied to simulate scenarios on carbon emission. • The carbon emission models of the power supply and demand sides are constructed. • Three scenarios for achieving the dual-carbon target are proposed. • The electric energy substitution and CCUS are investigated in the model. [ABSTRACT FROM AUTHOR]

Based on the full consideration of both domestic and global environments and conditions, the adherence to the conviction that lucid waters and lush mountains are invaluable assets, and the comprehensive weighing of pros and cons, the Chinese government has made a major strategic decision to achieve the goals of carbon peaking and carbon neutrality, which is crucial to the overall green transformation of the economy and society and the long-term benefits of the Chinese nation. With the new energy revolution and energy mix diversification driven by the carbon peaking and carbon neutrality goals, nonfossil energy sources centered on photovoltaic energy will gradually dominate the energy mix, while the status of electricity and hydrogen energy will be significantly enhanced and the consumption of coal and petroleum will decline significantly. Carbon peaking and carbon neutrality goals are both challenges and opportunities for China. Specific challenges are embodied in the large pressure of economic and energy restructuring, the rising manufacturing costs, the difficulties in withdrawing coal-fired power stations, the possibility of unstable grid operations due to large-scale integration of photovoltaic and wind power into the power distribution network, and the supply risks of key metals, while specific opportunities are emerging in the strong competitiveness of photovoltaic and wind power equipment, lower dependence on foreign petroleum and gas supply and accelerated low-carbon green transformation. China should strengthen the top-level design of the path to achieve carbon peaking and carbon neutrality, encourage innovation of green low-carbon technologies, accelerate economic and energy restructuring, strictly restrict the construction of new high-emission and energy-intensive projects, and steadily promote the adjustment and withdrawal of the existing high-emission and energy-intensive projects. [ABSTRACT FROM AUTHOR]

China is committed to peaking its carbon emissions by 2030 and become a carbon-neutral society by 2060. The building sector that accounts for over one-third of the total carbon emissions is expected to face a great challenge in helping China achieve this goal. Shenzhen, as a low-carbon pilot city, whether its low-carbon work of urban buildings reaches the target is crucial. An attempt has been made in this study to assess the intensity of carbon emissions and associated reduction efficiency of urban buildings (operation stage) in Shenzhen by using the life cycle assessment method. The results show that the total carbon emissions generated from the buildings' operation stage have increased from 22 million metric tons (Mt) CO 2 eq in 2005 to 42 (±13%) Mt. CO 2 eq in 2019. Carbon emissions mainly result from the buildings' electricity use (79%), followed by refrigerant release emissions (12%). The energy conservation and carbon emissions reduction intensity in Shenzhen is at the middle level in China, and there is considerable space for improvement. According to scenario-based analysis, the carbon emission of the buildings sector can probably reach its peak by 2025 with the implementation of suitable policies – 5 years earlier than national target by 2030. Overall, this study makes a systemic analysis of the characteristics of urban buildings energy consumption and carbon emissions reduction, which can provide supportings for justifying the effectiveness of low-carbon activities in Shenzhen and beyond. • This paper evaluated the emissions reduction of civil buildings in Shenzhen city. • LCA method is employed to quantify the efficiency of emissions reduction. • Public buildings consume more energy per unit area than residential buildings. • Electricity use is the focus of buildings emission, accounting for 79% of the total. • Current efforts cannot meet the ambitious carbon peak and neutrality target. [ABSTRACT FROM AUTHOR]