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The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

China's energy transformation has been accompanied by potential energy security issues. However, the influence degree and driving mechanism behind this phenomenon remain unclear. In this paper, we developed a complex network-based technical resilience assessment framework and quantified both the historical trajectory and the prospective pathway of energy technical resilience in China. We proposed to reveal the relationship between cleaner energy supply and technical resilience by defining three energy technology resilience indicators: energy robustness, energy recovery rate, and energy performance loss. The results showed a consistent decline in energy technical resilience within China, predominantly characterized by an average diminution of 6.83% in energy robustness from 2010 to 2018. Taking carbon neutrality as a target, China's energy system transformation, characterized by increasing clean energy supply, leads to a significant improvement of 7.73% in energy robustness from 2020 to 2060. Conversely, the nationally determined contribution scenario exhibited negligible changes (±0.57%) in energy robustness, while a decline of 2.21% was noted under the business as usual scenario. Furthermore, energy robustness exhibited increments of 12.07%, 2.3%, and 4.17% for every 10% rise in the share of clean energy supply during 2020–2060, demonstrating greater resilience for cleaner energy systems against disruptions, including energy import interruptions, malicious attacks, and random failures. It has been concluded that enhancing the supply of clean energy can significantly contribute to bolstering China's energy technical resilience, while simultaneously addressing climate change concerns. • Network analysis is used to assess China's energy technical resilience. • Energy technical resilience is evaluated regarding robustness, recovery rate, and performance loss. • Energy system robustness significantly increases under carbon neutrality scenario. • Increasing clean energy supply strengthens resilience against shocks. [ABSTRACT FROM AUTHOR]

Wind and solar power are expected to play important roles in many countries to achieve carbon neutrality; however, their inherent instabilities pose significant threats to existing power grids. Because hydropower has been recognized as a viable compensatory resource for solar and wind energy uncertainties, many studies have sought to determine optimal scheduling strategies for hydro-PV, hydro-wind, and hydro-PV-wind systems. However, few studies have simultaneously considered the sizing and scheduling of large-scale hydro-PV-wind hybrid systems connected to long-distance transmission lines. Therefore, this paper develops a mathematical metric to measure the wind and solar output complementarity and incorporates it into a multi-objective sizing and scheduling model for a hybrid hydro-PV-wind system, in which resource complementarity, profit generation, system reliability, and power curtailment trade-offs are concurrently considered. The ɛ -constraint method is employed and decision makers' attitude parameters are introduced to transform the proposed model into its equivalent single objective form. A case study in China reveals that the maximum wind and solar power output complementarity rate can be at least 0.19 for the studied hybrid hydro-PV-wind system. The incorporation of wind enables the hybrid hydro-PV-wind system to provide 4.11% more load than a hydro-PV system. In addition, if 2% of power curtailment is permitted, 1.53% more load can be served and 14.9% higher profits can be earned. To better explore the application of hybrid systems, management recommendations are provided for system operators, the power industry, and management administrations. • Multi-objective sizing and scheduling method for the hybrid renewable system is proposed. • Mathematical metrics to measure wind and solar power complementarity is provided. • A complementarity rate of 0.19 can be reached with 100% reliable service and no curtailment. • Integrating wind power into the hydro-PV system can reliably serve 4.11% more load. • An optimal rather than minimal level of the renewable power curtailment is more efficient. [ABSTRACT FROM AUTHOR]

The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

Substituting renewable energy for coal power under the carbon neutrality goal is an inevitable path for power system transformation, while the high share of renewable energy leads to a rapid rise in system flexibility demand. Currently, coal-fired power is still one of the most economical and reliable large-scale flexible resources in China. Therefore, the issue of how to mitigate the conflict between cleanliness and flexibility for coal-fired power is urgent to be solved. Accordingly, this paper established an optimization framework for the retirement and technology adoption of coal-fired power units with high spatial-temporal resolution, and the regional carbon emission budget is estimated from a top-down perspective. Secondly, a dynamic carbon quota allocation method considering technological innovation and adoption is proposed. The carbon trading cost of the units is evaluated from a bottom-up perspective, to realize the interaction between the obligatory policy constraints of the top-level target and the flexible incentives of the market mechanism. Finally, the empirical analysis was conducted in the Beijing-Tianjin-Hebei region of China. The optimal solution achieved by proposed method results in a substantial 27% reduction in total costs while keeping the increase in carbon emissions below 3%. By analyzing the dynamic evolution path of coal and the scale of diffusion of multi-type technologies, the differences in the roles coal power assuming at various stages of decarbonization are discussed. The robustness of the model and the validity of the proposed method were verified by comparing the dynamic/static carbon quota allocation. Overall, this study provides an effective optimization model for investigating the low-carbon technology adoption timing for coal power in Beijing-Tianjin-Hebei region, and offers valuable insights for other coal-dominated emerging economies seeking for power system transformation roadmap in the forthcoming net-zero carbon age. • A decision-making optimization model of CCPs low-carbon technology is proposed. • A dynamic CET system with upper-lower layer interaction is introduced. • The proposed dynamic CET system results in 27% reduction in total costs. • The high-resolution data of coal power in Beijing-Tianjin-Hebei region are used. • The strategy to solve the conflict between cleanliness and flexibility is given. [ABSTRACT FROM AUTHOR]

Carbon market is the most effective market tool for carbon emission reduction. China, the largest carbon emitter in the world, established the national carbon market in 2021, covering over 2000 key units in the power sector. Therefore, the forecasting of carbon price has profound implications for environmental and energy policies. In this paper, two traditional econometric model and three kinds of machine learning (ML) algorithms are used to predict carbon returns in the Chinese carbon trading market based on carefully selected predictors. Among all forecasting models, the Random Forest (RF) has the best forecasting performance, followed by some GARCH models using various factors. Compared with the traditional benchmark of ARMA model, the BP neural network (BP) and the GA improved BP method (GA-BP) are less competitive in predicting carbon returns in China because of their large forecasting errors. According to the optimal model, social attention to the carbon market, the international crude oil returns and the overall performance of the stock market are the most important predictors. The findings are robust to the change in the sample set. Overall, the ML approach shows an advantage in forecasting carbon returns, but the selection of predictors is also important. • Machine learning and econometric models are used to forecast carbon returns in China. • The selection of predictors is based on economic analysis. • The Random Forest (RF) outperforms the other ML and econometric methods. • The energy market is extremely important for predicting carbon returns. • Social concerns and stock market performance improve forecasting accuracy. [ABSTRACT FROM AUTHOR]

The allowance mechanism is one of the core and sensitive aspects in the design of a carbon emissions trading scheme and affects the compliance cost for each entity covered under the scheme. By examining China’s allowance mechanism from two aspects-allowance allocation and allowance distribution, this paper compares China’s carbon trading pilots with the EU Emissions Trading Scheme and California Cap-and-Trade Program. The comparison identifies the unique features in allowance mechanism and particular issues that affect the efficiency of the pilots. The paper also recommends courses of action to strengthen China’s existing pilots and to build valuable experiences for the establishment of the national cap-and-trade system in China. [ABSTRACT FROM AUTHOR]

• More reliable modeling assumptions and methods are proposed. • Price regulation may increase industrial output in China. • The reform can lead to a cleaner power mix and better economic structure. • The reform can be of great help to alleviate the fiscal deficit. • Impact mechanisms through cost linkages in different situations are put forward. China promised to achieve carbon neutrality in 2060. However, electricity prices are still regulated, which may affect the effect of carbon neutrality, especially in China, because 70% of China's electricity comes from thermal power generation. In this paper, a dynamic recursive computable general equilibrium model (CEEEA/CGE model) is applied to simulate the electricity market-oriented reform under the target of carbon neutrality from 2020 to 2060. The results show that the target may increase the demand for labor and capital to cope with increasing energy costs, and the reform may reduce GDP. However, power reform can significantly optimize the energy and economic structures and alleviate the fiscal deficit. Moreover, this paper finds and concludes the mechanism causing these effects (such as factor substitution, price advantage, government revenue, and structure), which provides policymakers with a new perspective of power reform. [ABSTRACT FROM AUTHOR]

As the world's largest producer and consumer of coal, coal phaseout is critical for China to achieve carbon neutrality and contribute to global decarbonization. Here, utilizing the Global Change Assessment Model, this paper simulates China's coal phaseout pathways at the sectoral level between 2015 and 2060, considering the deployment of carbon capture and storage (CCS). The results show that China's coal consumption peaks at 96.4 Exajoules (EJ) in approximately 2020 and needs to decrease to 10.9 EJ by 2060 to achieve carbon neutrality. Specifically, the power sector bears the most significant proportion of responsibility, as its coal consumption must fall to 1.4 EJ by 2060, which is approximately 97.2% lower than the peak in 2020. The industrial sector is next and needs to decrease consumption to 9.2 EJ of coal in 2060, which is a reduction of 32.3 EJ below the 2020 level. Electricity will replace coal as the dominant energy source in the industrial sector after 2040. However, a portion of feed coal will remain in the industry for a long time. Among the industrial subsectors, the steel sector needs to put significant pressure on efforts to phase out coal, followed by the cement and nitrogen fertilizer sectors. In 2060, these three sectors will consume 5.7 EJ of coal, accounting for 64.1% of the total coal consumption in the industrial sector. The deployment of coal-CCS is one of the alternatives to achieve carbon neutrality and ensure smooth coal phaseout. Under the carbon neutrality scenario, the annual deployment of coal-CCS needs to exceed 0.13 EJ from 2025 to 2045, with a peak at 3.3 EJ in 2045. • China's coal phaseout pathway is assessed in the carbon neutrality scenario (CNS). • Coal consumption needs to drop to 10.9 EJ in 2060 to achieve carbon neutrality. • The power sector's coal will decrease to 97.2% below the peak by 2060 in the CNS. • The industry will pass the power sector as the top coal consumer by 2045 in the CNS. • The deployment of coal-CCS will peak at 3.3 EJ after 2045 in the CNS. [ABSTRACT FROM AUTHOR]

Accurate and timely carbon emission accounting (CEA) is vital to industrial corporations, especially those who participate in the carbon market. With the rapid development of artificial intelligence and power systems, the power data-based method provides a new way for real-time CEA. However, the extensive installation of distributed photovoltaics (PV) significantly increases the accounting difficulty of corporate carbon emissions. This paper proposes a non-intrusive method of real-time CEA for industrial corporations from the perspective of modern power systems. First, a device operation state (DOS) estimation model based on a modified Informer algorithm is proposed to calculate corporate direct carbon emissions. Wherein, an equivalent distributed PV output estimation model is used to decrease the impact of invisible PVs on direct emission accounting. Second, an improved carbon emission flow model is proposed to calculate corporate indirect carbon emissions, which considers "prosumers" arising from the installation of distributed PVs. Finally, the total corporate carbon emissions, including direct and indirect parts, are obtained by using the CEA model. Case studies based on four typical high‑carbon-emission factories in Zhejiang province, China demonstrate that the proposed method can make accurate CEA for industrial corporations by effectively lessening the impact of distributed PVs. • A non-intrusive carbon emission accounting method for industrial corporations. • A novel perspective of modern power systems for carbon emission accounting. • Calculating carbon emissions considering distributed photovoltaics' impacts. • The method becomes superior as the photovoltaic output proportion increases. [ABSTRACT FROM AUTHOR]

• An energy-consumption permit trading scheme (ECPTS) was implemented in China in 2016. • This policy's contribution to reducing energy intensity is proved by a PSM-DID model. • The pilot ECPTS generated an average energy saving of about 43 Mtce in 2016–2019. • Different mediating effects of industrial structure and energy structure are proved. • A national ECPTS is suggested to promote linkage with the emissions trading scheme. The Chinese government implemented the energy-consumption permit trading scheme (ECPTS) pilot program in 2016, in order to achieve energy saving and carbon reduction. Based on the PSM-DID model, we attempt to test the effects of the ECPTS on energy consumption and energy intensity. With a counterfactual simulation, this paper also evaluates the potential gains and different mechanisms of this policy. The results show that, under the constraints of the established resources and policy efficiency, there is a positive relationship between the ECPTS and low-carbon economic transformation; the implementation of the above policy in the pilot provinces brought about an average energy saving of 43 Mtce during the period of 2016–2019. However, the potential gains of this policy need to be further increased to meet China's energy control targets. Additionally, a complete mediating effect of the energy structure indicates that the impact of the ECPTS on energy consumption and energy intensity depends significantly on energy structure adjustments. Finally, this paper highlights the key policy implications associated with the empirical results. [ABSTRACT FROM AUTHOR]

This paper explores the optimization scheme of carbon trading in China based on a novel energy-saving and emission-reduction (ESER) system with carbon price constraints. With the aid of nonlinear dynamics theory, the dynamics behavior of the novel system is discussed. Genetic algorithm and back propagation neural network is used to identify the quantitative coefficients according to the statistical data of the second period in European Union (EU). Taking the actual situation in EU for instance, the variables which are sensitive to carbon trading are detailedly researched. Enlightened by the EU’s experience, an optimal road of China ’ s carbon trading is put forward. The results show that carbon emissions could be controlled by carbon trading. The investment to carbon trading hampers economic growth in the near future, and ESER technical progress is negatively correlated with carbon trading in the long run. Demand and supply relationship is closely related to carbon price, both are the important issues in carbon trading system. Excessive government control and extortionate carbon price will deliver the opposite effect and even fatal influence on carbon trading system. [ABSTRACT FROM AUTHOR]

• A general charging scheduling problem for an electric bus fleet, which can be used a building block of more complex electric bus system planning or operations problems. • Time-based charging windows, battery state-of-charge bounds, time-of-use charging tariffs, and station-specific electricity load capacities considered. • A mixed linear integer programming model with charging location and time decisions specified by location-specific charging time slots. • A Lagrangian relaxation framework for decomposing the fleet charging scheduling problem by individual buses. • A bi-criterion dynamic programming algorithm for each single-bus charging scheduling subproblem. Electrification poses a promising low-carbon or even zero-carbon transportation solution, serving as a strategic approach to reducing carbon emissions and promoting carbon neutrality in the transportation sector. Along the transportation electrification pathway, the goal of carbon neutrality can be further accelerated with an increasing amount of electricity being generated from renewable energies. The past decade observed the rapid development of battery technologies and deployment of electricity infrastructure worldwide, fostering transportation electrification to expand from railways to light and then heavy vehicles on roadways. In China, a massive number of electric buses have been employed and operated in dozens of metropolises. An important daily operations issue with these urban electric buses is how to coordinate their charging activities in a cost-effective manner, considering various physical, financial, institutional, and managerial constraints. This paper addresses a general charging scheduling problem for an electric bus fleet operated across multiple bus lines and charging depots and terminals, aiming at finding an optimal set of charging location and time decisions given the available charging windows. The charging windows for each bus are predetermined in terms of its layovers at depots and terminals and each of them is discretized into a number of charging slots with the same time duration. A mixed linear integer programming model with binary charging slot choice and continuous state-of-charge (SOC) variables is constructed for minimizing the total charging cost of the bus fleet subject to individual electricity consumption rates, electricity charging rates, time-based charging windows, battery SOC bounds, time-of-use (TOU) charging tariffs, and station-specific electricity load capacities. A Lagrangian relaxation framework is employed to decouple the joint charging schedule of a bus fleet into a number of independent single-bus charging schedules, which can be efficiently addressed by a bi-criterion dynamic programming algorithm. A real-world regional electric bus fleet of 122 buses in Shanghai, China is selected for validating the effectiveness and practicability of the proposed charging scheduling model and algorithm. The optimization results numerically reveal the impacts of TOU tariffs, station load capacities, charging infrastructure configurations, and battery capacities on the bus system performance as well as individual recharging behaviors, and justify the superior solution efficiency of our algorithm against a state-of-the-art commercial solver. [ABSTRACT FROM AUTHOR]

Using China’s provincial panel data and national panel data of OECD (Organization for Economic Co-operation and Development) and BRICS (Five major emerging national economies: Brazil, Russia, India, China and South Africa), this paper simulates the scenario-based potential effect of carbon trading in China. Analysis methods included Stochastic Frontier Analysis, Difference-in-differences Model, and Nonlinear Programming Technique. Results indicated that in a theory-based view of carbon trading, the shadow price of carbon dioxide generally rises, with a non-linear negative correlation with carbon dioxide emissions. In different regions, the shadow price of carbon dioxide presents a digressive tendency among eastern, central, and western areas, with divergent gaps between and within areas. When the greatest goal is assumed to reduce national carbon intensity as much as possible at the given national GDP (Gross Domestic Product) (Scenario I), carbon trading has the effect of reducing carbon intensity by 19.79%, with the consideration of Porter Hypothesis effect. If the rigid constraint of national GDP is relaxed, and the dual constraint of both economic growth and environment protection in each region is introduced (Scenario II), the resulting effect is a reduced carbon intensity of 25.24%. China’s general carbon intensity in 2012 was higher than goals set at the Copenhagen Conference, but lagged behind the goal of Twelfth Five-Year Plan for National Economy. This study provides realistic and significant technical support for the government to use in designing and deploying a national carbon trading market. [ABSTRACT FROM AUTHOR]

• We provide the most up-to-date CO 2 emission inventory for China's cement industry. • We construct the G-LEAP model to project the CO 2 emissions of China's cement industry. • The CO 2 emissions could be reduced maximum by 70 percent in 2060, while the remainder will need to come from technological innovation and new growth horizons. This paper aims to assess the emission reduction potential of combined mitigation technologies in China's cement industry, thus transitioning towards carbon neutrality through a suitable technology portfolio and exploring a low-carbon pathway. The G-LEAP model is constructed for future carbon emission projection, incorporating the cement demand projection and technology application. The pathways based on different technology portfolios are developed to estimate the CO 2 emission trajectory of China's cement industry. We maximize the utilization of currently available technologies and assume a high level of innovative technology diffusion rate in the integrated mitigation pathway to explore the maximum abatement potential of the cement industry. The result shows that short-term mitigation mainly relies on improving energy efficiency and alternative low-carbon fuels, which would contribute 9–12% and 17–22% of the cumulative emissions reduction in the integrated mitigation pathway compared to a frozen scenario. Alternative clinkers can significantly reduce process-related emissions, but the potential is determined by the availability of raw materials, which would contribute 30–39% of the cumulative emission reduction. Post- and oxygen-combustion capture is expected to be deployed by 2030 and contribute about 28–44% to cumulative emission reduction. The technology portfolio in the integrated mitigation pathway would reduce China's cement CO 2 emissions by 63–73% compared to the frozen scenario, and the remainder of CO 2 emission would be 300–400 Mt in 2060, which will need the technological innovation and new growth horizons, such as carbon sink approaches or carbon trading. [ABSTRACT FROM AUTHOR]

• The influencing factors of carbon prices in China's carbon market are analysed. • The influencing factors and historical data are combined to predict the carbon price. • The EEMD-BP-ELM model and the CHINAGEM-E model are constructed and combined. Carbon price is one of the core indicators of the carbon market. Making accurate predictions of carbon prices based on insight into the volatility characteristics of the carbon market will not only provide a scientific basis for investors and regulators to make decisions and greatly promote emission reduction, but also effectively promote the healthy development of the carbon financial market. Based on the historical carbon price data of the national carbon market, this paper constructs the EEMD-BP-ELM model to predict the high and low frequency components of China's future carbon price. On this basis, according to China's goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, the CHINAGEM-E model is used to predict the trend component of the carbon price under the baseline and the 2060 carbon neutral scenarios. Finally, the trend component is combined with the high and low frequency components to obtain the price range from 2022 to 2060. We find that under the baseline scenario, China's carbon price range in 2060 is [343, 785] CNY/tCO2, while under the 2060 carbon neutral scenario, this price range is [1543, 3531] CNY/tCO2. [ABSTRACT FROM AUTHOR]

• Constructing a new welfare function considering economy, equity and ecology. • Optimizing carbon emission pathways from multiple perspectives. • Focusing on the emission pathways in China achieving the ambitious targets. With the increasing climate warming, reducing carbon emissions has gradually become a consensus around the world. However, the process of carbon emission reduction will inevitably lead to the transformation of economic structure, industrial structure and energy structure, which will bring huge changes in economic output, employment and other aspects, thus affecting the overall social welfare and equity. Therefore, it is particularly important for each country to determine a scientific and effective carbon emission pathway which take into account both social development and climate targets. As the largest carbon emitter, China has put forward the targets of carbon peak and carbon neutrality. In this regard, the corresponding carbon emission reduction scenarios are designed according to the new situation of China's emission reduction in this paper, firstly. Furthermore, under the premise of maximizing social welfare, an optimization model which integrating economic growth-equity-environment is developed to determine China's emission pathways under different scenarios from 2020 to 2050. Besides quantitatively showing the future total amount and peak value of carbon emissions under different scenarios, the results can also identify its evolution pathway over time. Therein, the peak under most scenarios will concentrate in 2025–2030, which is about 10.7 to 11.8 billion tons. With the increasingly stringent constraints on carbon emission reduction, the cost of emission reduction will increase correspondingly, but the overall social welfare will increase slightly. Finally, our findings provide practical guidance on achieving carbon emission reduction and enlightenments on policymaking. [ABSTRACT FROM AUTHOR]

The prioritized purpose of this dissertation is to put forward a scientific and plausible interval of carbon price benchmark on the unified carbon trading market, of which will be scheduled to be established in the year of 2016. A model named EMD–GARCH that integrated Empirical Mode Decomposition (EMD) with Generalized Autoregressive Conditionally Heteroskedastic (GARCH) together is presented here in order to forecast the carbon price of the five pilots (Shenzhen, Shanghai, Beijing, Guangdong, and Tianjin) in 2016 so as to provide exogenous data for later analysis. For further study, a recursive dynamic Computable General Equilibrium (CGE) model will be structured to explore the impacts on China’s macroeconomics, environmental quality, and energy demand respectively under 8 different simulation scenarios, which are being designed in terms of the aforementioned calculated carbon price outcome. The simulation results illustrate that the unified carbon price policy will exert a comprehensive effect on China in various perspectives that we discussed in this paper. Moreover, regarding the carbon price scenarios, the price level by itself generates a different degree of impacts in improving the environmental quality, decreasing the energy demand, and increasing the macroeconomic growth. Consequently, based on the analysis of those three approaches mix in this study, we suggest the alternative interval of carbon price benchmark in the national unified carbon trading market with a lower bound of 30 yuan/tCO 2 to an upper bound of 50 yuan/tCO 2 . [ABSTRACT FROM AUTHOR]

• Statistical analysis of hourly wind speed series. • Finding out the optimal distribution to characterize the hourly wind speed series. • Guiding wind farm operation with the hourly expected wind power. Accurate wind speed characterization is important for wind farms to efficiently utilize wind energy which plays a significant role in reducing the dependence on fossil fuels and promoting global carbon peak and carbon neutrality. Since hourly wind speed density exhibit a variety of shapes, their characteristic features change substantially within a day, influenced by meteorological factors, therefore, the hourly wind speed analysis is considered to improve the accuracy of wind speed characterization. In this paper, 4 commonly used wind speed probability distributions (Weibull, Rayleigh, Gamma, Lognormal distribution) are applied for hourly wind speed analysis, and the best fitting distribution of each hourly wind speed series is identified by the goodness-of-fit test. Then the hourly wind power distribution of wind farms is determined by the corresponding optimal hourly wind speed distribution and the relation between wind power and wind speed. Finally, the hourly expected wind power calculated by the hourly wind power distribution is used to guide the hourly operation of the wind farm. The measured wind speed and wind power data of Dashuikeng wind farm in the Ningxia Autonomous Region of China is taken as an example to illustrate the results, 1) the hourly wind speed analysis performs a more accurate characterization of wind speed than annual wind speed series analysis. 2) The range of the average value of hourly wind speed series is 5.7 m/s–6.9 m/s, and the average wind speed, overall, during the daytime is smaller than that during the nighttime. 3) Gamma distribution shows a better performance than the other three distributions in characterizing the hourly wind speed series. 4) Wind energy resources are not fully consumed during the daytime, especially the period of 11:00–18:00. The proposed method improves the characterization of wind speed and guidance of the hourly operation for the wind farm, which has an important significance in raising the efficiency of wind energy utilization. [ABSTRACT FROM AUTHOR]

• Carbon emissions trading inhibits green technology innovation in the short run. • Carbon emissions trading will reduce the carbon emissions and carbon intensity greatly at this stage. • Carbon emissions trading has a crowding-out effect on R&D investment and increases the carbon trading price, which inhibited green technology innovation. • It is necessary to control the scale of carbon emission quota and diversify the subjects of carbon trading. Carbon emissions trading is an important measure to promote high-quality economic development. Based on the panel data of 30 provincial administrative regions in China from 2008 to 2017, this paper uses the difference-in-differences method to analyze the impact of carbon emissions trading on green technology innovation. The results show that: (1) Carbon emissions trading inhibits green technology innovation in the current stage, but greatly reduces carbon emissions and carbon intensity; (2) Carbon price and R&D investment are the mainly working channels, carbon emissions trading has a crowding-out effect on corporates' R&D investment and increases the carbon trading price, which in turn inhibits green technology innovation; (3) Carbon emissions trading has a stronger inhibitory effect on green technology innovation in eastern regions and regions with low emission intensity. Local government competition positively moderates the green technology innovation effect of carbon emissions trading. However, the command-controlled environmental regulations and the scale of the carbon emission quota have opposite effects, with the compatibility of environmental regulation tools remaining to be resolved. Therefore, it is necessary to control the scale of carbon emission quota to diversify the subjects of carbon trading and strengthen the impact of market-incentive environmental regulation tools. [ABSTRACT FROM AUTHOR]

• A multiagent system dynamics model is established. • The impact of different policies on the diffusion and benefits of EVs is studied. • EV diffusion is most significantly impacted by licence plate restrictions. • The health benefits of EVs are the greatest. • The benefits of government subsidies are positive. Electric vehicle (EV) diffusion plays a vital role in transforming and upgrading China's automobile industry by ensuring energy security, improving air quality, and contributing to achieving the goal of carbon neutrality by 2060. EV diffusion is a complex dynamic system that involves various agent interactions. Nevertheless, previous research has rarely focused on the impact of different policies on EV diffusion under multiagent interactions and thus has not systematically evaluated its diffusion benefits. This paper establishes a multiagent system dynamics (SD) model to explore the impact of different policies on the diffusion and benefits of EVs. The results indicate that (1) EV diffusion is most significantly impacted by licence plate restrictions, following by charging pile construction subsidy, most sensitive to purchase subsidy policies, and least affected by government R&D subsidies. (2) Moreover, all four policies decrease EV energy benefits but positively impact the carbon emission reduction, health, and social benefits of EVs. The health benefits of EVs are the greatest, followed by the carbon emission reduction benefits. (3) The benefits of government subsidies to EV manufacturers, consumers, and charging infrastructure operators are positive. Finally, by integrating the development situation of China's EV industry, feasible policy suggestions are proposed for sustainable development. [ABSTRACT FROM AUTHOR]

• Propose a new real option model for China's shale gas investment decision-making. • Consider multiple uncertainties in market, technology and environment. • Explore investment values and optimal investment timings under different scenarios. • Study the impacts of changes in relevant factors on investment decision-making. The investment in shale gas green and efficient development in China faces many uncertain factors that affect the investment decision-making of enterprises, which contributes to China's low-carbon energy transformation and national energy security. Based on the characteristics of China's shale gas resource development and considering the uncertainties in the aspects of market, technology and environment, this paper proposes a real option model to evaluate investment decision-making of China's shale gas development and uses the Least Square Monte Carlo simulation to solve the investment values and optimal investment timings of shale gas low, moderate, high and ultrahigh productivities under four combination scenarios of financial subsidy and/or carbon emission trading. The results show that the current investment environment in China is not suitable for immediate investment in shale gas development projects. Investors can invest immediately when the initial average daily production reaches at least 30 × 104 m3. And financial subsidy and carbon emission trading help advance the optimal investment timings. According to a sensitivity analysis, appropriate increases in financial subsidy, shale gas price, absolute value of the drift rate of drilling and completion cost and environmental external benefit coefficient are found to improve the investment value and advance the optimal investment timing, while drilling and completion cost and production decline rate have the opposite effect. Therefore, to stimulate the investment, the government and enterprises should adopt some measures such as addressing key problems on technology and equipment, flexibly adjusting subsidy policies and driving forward the carbon emission trading market. [ABSTRACT FROM AUTHOR]

Abstract: Photovoltaic (PV) is one of the promising solar energy applications. Measured data can give the realistic performance of PV systems under actual operating environments for product selection and system design. This paper studies a grid-connected PV system installed in an institutional building. Technical data including available solar radiation and output energy generated were systematically recorded and analyzed. The performance of the PV system is elaborated in terms of energy, environmental and financial aspects. Feed-in-tariff (FIT) schemes with high electricity selling price can encourage the deployment of PV system. The monetary payback period (MPBP) for the PV system was computed to be 72.4years when the electricity buying price is equal to the electricity selling price. If carbon trading was considered, the payback was shortened to 61.4years. The embodied energy payback period (EEPBP) was estimated to be 8.9years. The findings would be useful for planning grid-connected PV installations and applicable to other spaces with similar architectural layouts. [Copyright &y& Elsevier]

• It evaluates the effect of China's ETS on non-fossil energy development. • It analyzes ETS's effects from energy type, pilot, and pilot-energy type aspects. • The ETS has the most significant promoting effect on hydropower and photovoltaics. • Carbon price plays a positive role in moderating the effect of China's ETS. Emissions Trading Scheme (ETS) can internalize the environmental costs of fossil energy consumption and encourage enterprises to consume non-fossil energy, which is crucial for People's Republic of China (PRC) to achieve the contribution target of the Paris Agreement cost-effectively. However, whether the ETS in PRC can promote non-fossil energy development remains to be answered. This paper uses a difference-in-differences model to evaluate the effect of the ETS on non-fossil energy development in PRC. Using provincial monthly panel data from January 2004 to December 2019, we find that (1) the ETS has significantly promoted non-fossil energy development in PRC, increasing the monthly average share of non-fossil energy power generation in total power generation by 2.326 percentage points, which has been verified by a series of robustness tests. (2) The effect of the ETS on non-fossil energy development is heterogeneous by energy type, pilot region, and pilot-energy type. The ETS has significantly increased the share of hydropower and photovoltaic power generation, and has significantly promoted non-fossil energy development in Guangdong; meanwhile, the ETS has significantly increased the share of hydropower and nuclear power in Guangdong, and the share of wind power and photovoltaic power in Hubei. (3) The higher the carbon price, the greater the role of the ETS in promoting non fossil energy development. [ABSTRACT FROM AUTHOR]

[Display omitted] • Quantitative methods for power flexibility. • Decentralized and coordinated model for sharing hydropower flexibility in interconnected power systems. • New linearization methods for complex nonlinear constraints of hydropower system and DC tie-lines. • The method and model are performed on the China Southern Power Grid. In 2020, China pledged to become carbon neutral before 2060, which implies the acceleration of China's low-carbon energy transition and development of new energy (wind and solar). As a result, the demand for flexibility in China's power system has never been so urgent. Because China has the greatest installed hydropower capacity in the world, sharing hydropower flexibility in its interconnected power systems is of great practical significance to China. This paper reports the necessity and feasibility of sharing the hydropower flexibility of Yunnan's large hydropower bases in the China Southern Power Grid (CSG) through direct-current (DC) tie-lines. In addition, an effective solution to this problem is presented. First, several quantitative methods of flexibility are proposed. Second, a decentralized and coordinated scheduling model that taps and shares the power flexibility of large hydropower bases in interconnected power systems is constructed. The model considers the complex nonlinear constraints of hydropower and DC tie-lines. Third, new linearization modeling methods are proposed to transform the model to a mixed integer linear programming (MILP) model, reducing the computational complexity. The MILP model is applied to the CSG to determine the hourly generation schedules for power plants in Yunnan's large hydropower bases and the power transmission plans for DC tie-lines. The cases studies demonstrate that the proposed model can fully tap hydropower flexibility and ensure that large hydropower bases simultaneously respond to the flexibility demands of sending-end and receiving-end power grids. The study provides a valuable technical approach for alleviating the lack of power flexibility in other countries with large hydropower bases. [ABSTRACT FROM AUTHOR]

• The effects of multi policies on electric vehicle diffusion were investigated. • A government–enterprise–consumer system dynamics model was developed. • Abolishment of purchase subsidy can lead to a decline in electric vehicle sales. • Other policies can make up for the negative impact of purchase subsidy abolishment. A comprehensive understanding of how the policy mix influences the market diffusion of sustainable technology is a critical focus of recent research. Electric vehicles are considered to have the potential to address energy depletion, climate warming, and air pollution. This study attempts to investigate the effect of multiple policies, namely, product subsidy, carbon emission trading, and license plate restriction policies on electric vehicle diffusion once the purchase subsidy scheme is phased out in China. Thus, a system dynamics model that considers the interaction explicitly among the government, enterprises, and consumers is proposed. Multiple scenarios are adopted to analyze the policy effect on electric vehicle diffusion. Findings reveal the market share of electric vehicles in China will drop by 40.39% in 2020 if the purchase subsidy scheme is abolished. The three policies discussed in this paper can make up for the negative impact of purchase subsidy policy abolishment and promote the long-term growth of EVs. However, according to an in-depth analysis of the policy mechanism, these policies should also be adjusted according to the actual situation and their roles can only be optimized by combining them with other policies. [ABSTRACT FROM AUTHOR]

• A modified dynamic recursive CGE model is constructed. • Renewable energy growth is considerable only if ETS revenue is used. • Broader renewable energy subsidies are better than ones with narrow scope. • A small part of ETS revenue should be used to subsidize residents for social stability. Emission Trading Scheme (ETS) and renewable energy generation are emission reduction methods in most countries in the world. However, few studies have focused on the impact of ETS on renewable energy. The question is, can carbon trading promote renewable energy generation? This paper first analyzes different distribution strategies of ETS revenue by applying dynamic recursive computable general equilibrium model with multi-sectors. Practical scenarios and better options of distribution of ETS revenue by a comprehensive evaluation based on entropy weight method are proposed. The results show that ETS with no subsidy to renewable will reduce the demand for energy, increase the cost of renewable energy sources and decrease the generation. ETS will be the spring of renewable energy generation when most of the revenue is used for all kinds of renewable energy sources, instead of some of them. The growth of renewable energy generation is also substantial. It is necessary that a small portion of ETS revenue should be used to subsidize residents to reduce the gap between the rich and the poor. If this income is used for government investment and consumption, it will also help to mitigate economic losses, which is caused by the direction of investment by the Chinese government. [ABSTRACT FROM AUTHOR]

• The potential gains and carbon emissions reduction of carbon trading are detected. • Three scenarios of carbon trading schemes are considered and compared. • Sectoral trading may create potential gains of 268.02 trillion yuan in 2006–2015. • Carbon emissions may decrease by 17.17 billion tonnes under sectoral trading scheme. • Sectoral trading decreases industrial carbon intensity by 19.80% during 2015–2020. Carbon trading is an important market tool in driving growth and carbon dioxide emissions reduction in industrial sectors in China. This paper attempts to assess the impacts of carbon trading on economic output and carbon dioxide emissions reduction in China's industrial sectors by employing the data envelopment analysis (DEA) based optimization models, based on three carbon trading schemes, i.e., no trading (NT), sectoral trading (ST), and sectoral-and-temporal trading (STT) during 2006–2015. Comparing with the no-trading scheme, the results indicate that, (1) the ST and STT schemes may create potential gains of 268.02 and 612.26 trillion yuan in the whole industrial during the study period, i.e., the industrial value-added would be increased by 55.17% and 73.76%, respectively; (2) the ST and STT schemes could reduce 17.17 and 19.22 billion tonnes of carbon dioxide emissions, respectively, accounting for 58.30% and 65.25% of emissions reduction in the whole industry; and (3) if carbon trading were adopted in China since 2006, its carbon intensity of industrial sectors would decrease by 34.89%, 47.44% and 19.80% under the ST scheme, and 58.93%, 31.50% and 10.25% under the STT scheme during the11th, 12th and 13th Five-Year Plan periods, respectively. [ABSTRACT FROM AUTHOR]