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Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

Preventing the impact of natural disasters on urban-rural income inequality helps to achieve shared prosperity. This paper uses the functional entropy weight method to calculate the level of natural disasters from 2004 to 2020. Then, this paper discusses the impact of natural disasters on the urban-rural income gap. The empirical results are as follows. (1) Natural disasters have expanded the urban-rural income gap. The regression coefficient is 0.2663, which is significant at the 1% level. (2) In areas with a high proportion of the rural population, the impact of natural disasters is more prominent. (3) Mechanism analysis indicates that natural disasters have widened the urban-rural income gap by restraining rural investment and agricultural production. Finally, this paper suggests strengthening investment in the agricultural sector to mitigate the negative impact of natural disasters. • This paper uses the functional entropy weight method to calculate the level of natural disasters. • This paper quantitatively measures the impact of natural disasters on the urban-rural income gap. • This paper examines the mechanism of the impact of natural disasters on the urban-rural income gap. • This paper provides policy suggestions for narrowing the urban-rural income gap. [ABSTRACT FROM AUTHOR]

Abstract Global climate change is a significant environmental problem. A major trigger of climate change is the excess carbon emissions. Based on 44 scenarios in the second generation of new dry cement technology systems, this paper establishes IPSO-BP model to forecast the carbon emissions peak of China's cement industry for 2016–2050 years. The results indicate that China's cement industry only implements capacity reduction plans and the second generation of new dry cement technology systems, so that carbon emissions can reach the peak before 2030. It is up to 19 years ahead of the carbon emissions peak of the basic scenario and the carbon emissions peak is reduced by 38 Mt. Moreover, this paper analyzes the technical combination of the earliest carbon emissions and the lowest carbon emissions. As for the earliest carbon emissions technical combination, China's cement industry carbon emissions will peak at 789.95 Mt in 2021. According to the lowest carbon emissions technical combination, China's cement industry carbon emissions will peak at 742.37 Mt in 2025. Accordingly, the conclusions will be helpful for making carbon emissions reduction policies for China's cement industry. Highlights • Carbon emission levels were reviewed from industrial output and technologies. • The prediction model of carbon emissions was built with improved PSO and BPNN model. • The scenarios were set to analyze carbon emission peak in China's cement industry. • Importance of technique and capacity reduction in China's cement industry is defined. • The best carbon emission peak plan for China's cement industry is discussed. [ABSTRACT FROM AUTHOR]

How to achieve the "double carbon" goal in energy systems has been the concern of governments. Integrated energy system (IES) is affected by climate change during his operation, in order to study the impact of climate change on IES and achieve the "double carbon" goal in energy systems, this paper proposes an integrated machine learning(IML) to forecast the long-term load, then investigates IES costs and carbon emissions in relation to climate, followed by the establishment of carbon peak energy system(CPES) and carbon neutral energy system(CNES), finally the honey badger algorithm is used to optimize the configuration of CPES and CNES. The results show that: IML can accurately make load forecasts. Under climate change, changes in load reduce the cost and carbon emissions of IES, and changes in equipment efficiency increase the cost and carbon emissions of IES. When both are considered, the cost and carbon emissions of IES increase by 1.18% and 0.92% per decade respectively. The costs of CPES and CNES increase by 0.93% and 1% respectively for every 10 years earlier than the year of achievement. To meet China's "double carbon" goal, CPES and CNES need to increase their costs by 1.97% and 2% respectively. • Integrated machine learning proposed for long-term load forecasting. • Studied the impact of system equipment efficiency and load with climate change. • Proposing carbon peaking energy system to achieve the carbon peak goal. • Proposed carbon-neutral energy system to achieve carbon neutrality goal. • The increased costs of achieving carbon peak and carbon neutral were studied. [ABSTRACT FROM AUTHOR]

Abstract The aim of this paper is to evaluate and compare the techno-economic performance of grid-connected photovoltaic (PV) power systems for a rooftop solar PV building containing 14 families in five climate zones in China. The techno-economic performance of grid-connected PV system in the five regions was evaluated using the HOMER software. Monthly average electric production, economic and environmental considerations, and sensitivity analyses were all considered. The results show that the pollutants from grid-only, grid/PV, and grid/PV/battery systems come mainly in the form of CO 2 emissions. In addition, this study concludes that grid/PV systems are technically, economically and environmentally feasible for all five climate zones. The excess electricity, NPC, and COE values of the grid/PV systems for all five climate zones increased with PV penetration increased, whereas the CO 2 emissions for these climate zones decreased due to the increasing PV sizes. For the grid/PV systems of five climate zones, Kunming is the most economical with the least NPC ($113,382) and COE ($0.073/kWh). The lowest CO 2 (38,975 kg/yr), SO 2 (35.4 kg/yr), and NO x (165 kg/yr) emissions of grid/PV systems occurred in Kunming. From an economic and environmental perspective, Kunming, with its mild climate conditions, may be especially suitable for grid/PV power generation. Highlights • The techno-economic performance of grid/PV system is evaluated using HOMER in five climate zones. • The grid/PV systems are feasible in five climate zones, China. • Kunming is the most economical with the least NPC ($113,382) and COE ($0.073/kWh). • Kunming may be especially suitable for grid/PV power generation. • The grid/PV system is a better choice for all five regions than the other studied systems. [ABSTRACT FROM AUTHOR]

Wind resource in China is abundant due to its vast land mass and long coastline. Based on wind speed and direction records from wind measurement towers at six onshore sites with different geographical climate conditions in China, statistical assessment of wind characteristics and wind energy potential at height of 70 m corresponding to the hub heights of multi-megawatt wind turbines is presented and discussed in this paper. First of all, the Weibull distribution function is verified to be a reliable model for wind speed prediction and the moment method is proved to be an accurate approach for estimation of the Weibull parameters at all the sites. Moreover, the variations of mean wind speed, the Weibull parameters and wind power density at the six sites are investigated in terms of seasonal, monthly and diurnal time scales. Finally, annual energy outputs at the six sites are determined by taking a commercial wind turbine as an example to evaluate the economic feasibility of wind energy development. The results of this study indicate that the six sites in North China, Northeast China, Central China, coastal regions in East China and Southeast China are in rich wind energy areas which are suitable for wind energy utilization. In particular, among the six sites, Tongliao in North China is the most promising location for wind energy development with the maximum annual energy output of 10.33 GWh. The objective of this study is to provide useful information for wind energy development and an effective approach for wind energy potential assessment. [ABSTRACT FROM AUTHOR]

Recently the expansion of urban-rural income inequality and the rapid growth of fossil-energy consumption have become two significant challenges China should address to achieve its ambitious goals. For the first time, this study uses the Chinese provincial panel data during the period of 1996–2013 to perform a quantitative analysis on the relationship between the urban-rural income gap and the per capita electricity consumption. Fully considering the potential endogeneity problem, this paper uses the orthogonal-difference Generalized Method of Moments (GMM) as the benchmark estimation method. To ensure the robustness of the estimations, the Theil index and per capita urban-rural income are utilized to measure the urban-rural income gap. The estimation results indicate that the influences of urban-rural income inequality on electricity depend on the income level. At the current stage of economic development, the income disparity has significantly negative impacts on provincial per capita electricity consumption. Moreover, there is also strong evidence for the existence of the inverted U-shaped relationship between per capita electricity consumption and GDP per capita. Furthermore, other economic and social factors, including the enhancement of urbanization and industrialization levels, the adjustment of the population structure, and the development of import-export trade may also promote electricity consumption. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviewed the history of the evolution of low-carbon development strategies and the establishment of low-carbon targets in China, over the past 30 years. The climate policy development in China has been classified into four stages and the key strategies of the Chinese government adopted towards low-carbon development were analysed. The results showed the changing view of the government regarding the climate issue, was the underlying reason for the establishment of low-carbon development targets, whilst a low-carbon economy is now receiving increasing attention in the governance of China. Some recommendations for future improvement were given at the end of the paper. [Copyright &y& Elsevier]

The threat of global climate change has caused the international community to pay close attention to atmospheric levels of greenhouse gases such as carbon dioxide. Transportation sector carbon dioxide emissions efficiency (TSCDEE) is a key indicator used to prioritize sustainable development in the transportation sector. In this paper, the epsilon-based measure data envelopment analysis model with undesirable outputs is applied to estimate TSCDEE for 30 provinces in China from 2010 to 2016. We also analyze influencing factors using the spatial Durbin model. Research shows that the overall TSCDEE of the Chinese provinces studied was 0.618, indicating that most regions are still in need of improvements. The provinces with the highest TSCDEE are located in developed coastal regions of China. This study shows that factors such as transportation structure, traffic infrastructure level, and technological progress have prominent positive effects on TSCDEE, while both urbanization level and urban population density exert significantly negative effects on TSCDEE. The findings should have a far-reaching impact on the sustainable development of global transportation. [Display omitted] • Providing more accurate method in estimating CO 2 emissions of the transport sector. • Focusing on the transportation sector carbon dioxide emissions efficiency (TSCDEE). • Technological progress is the key to improve TSCDEE among different factors in China. • Higher mode share of water and rail transport contributes to higher TSCDEE in China. • Urbanization presents significantly negative effect on TSCDEE in China. [ABSTRACT FROM AUTHOR]

Abstract: Actual operation energy consumption of the high energy efficiency buildings built and operated in China and U.S. has been quite different than expected. This paper compares actual energy consumption to expect high energy efficiency office buildings in U.S. and China. Considering the different indoor design temperature, climate conditions and operated period between the compared cases in the two countries impact on the building energy consumption, correction model was built to eliminate the influence of the three factors on the comparison result and put the comparison analysis of high energy efficiency office buildings in the two countries into the same level. Regard to building general information and climate condition, four pairs of buildings in typical climate zones of China and U.S. were selected to compare the building energy conservation technology and building energy consumption based on a large scale of investigation and testing. After corrected, the energy consumption data are analyzed, including total energy consumption, and sub-metering energy consumption such as heating, cooling, lighting, office equipment, etc.. The energy saving technologies applied in these four pairs of buildings was also compared to explain energy consumption differences. [Copyright &y& Elsevier]

Abstract: China is a large developing country with high carbon intensity. It is likely that China will have to face the challenge of CBTA (carbon motivated border tax adjustments) in future. Meanwhile, CBTA would create price gap between CBTA users and target countries, and change the competitiveness of different producers. Under such circumstances, this paper seeks to contribute to the debate on CBTA by focusing on the potential impacts of CBTA on different regions of China from a perspective of competitiveness. We adopt two geographical divides in China (eastern–central–western zones, high-trade-openness to low-trade-openness regions), and simulate the potential impacts across regions through a general equilibrium model. Our simulation results show that CBTA would affect competitiveness of different producers, their comparative advantages, relative trade shares, outputs and emissions. CBTA would induce structure change of the economy, and result in a shift of industrial output toward non-industrial output. There are significant differences in the effects of CBTA among different regions in China. CBTA would result in a relocation of outputs across regions in China. Therefore, CBTA would also have impact on China’s regional development policy. [Copyright &y& Elsevier]

Abstract: Climate change poses huge challenges to the sustainable development of human society. As a major CO2 emission source, decarbonization of power sector is fundamental for CO2 emission abatement. Therefore, considering the “carbon lock-in” effects, it’s critical to formulate an appropriate roadmap for low-carbon generation technologies. In this paper, key low-carbon technology solutions are firstly identified according to their developing prospects and the fundamental realities of China’s power sector. Then, costs, reduction effects and potentials for the key technology options are evaluated. On this basis, typical scenarios are selected and a scenario set is established which identifies and incorporates the key low carbon factors, and a multi-scenario analysis is implemented to China’s power sector based on a comprehensive power mix planning model. Then, contributions of CO2 reduction among the key technology solutions are revealed. Prospect for CO2 emission reduction is discussed, which informs the possible emission trajectories towards 2030. Finally, low-carbon technology roadmaps under specific scenarios are elaborated, which implies corresponding optimal evolution of power generation mix. [Copyright &y& Elsevier]

Abstract: Forecasts of energy demand, the fuel mix meeting that demand and the associated emissions are a key requirement for informed energy planning and policy decisions to ensure energy security and address climate change. While there have been many studies on China focusing on the short and medium term (to 2020 and 2050) there is little in the literature focusing on the long term (to 2100). This paper seeks to address those gaps on sectoral energy demands and emissions on long term by following a two-stage approach. It develops key energy indicators on useful energy demand, transport mobility and end use fuel demand for various sectors. The main drivers of these indicators are socio-economic parameters. The indicators are used to project energy service demands and emissions forward for China in TIMES G5 model at least cost approach. The results from this reference scenario suggest that China will require approximately 4 Gtoe of primary energy, by the end of the 21st century to deliver 3 Gtoe final energy consumption, 10 PWh of electricity generation, 1.3 Gtoe of energy imports, which will results in 10 Gt CO2 emissions. [ABSTRACT FROM AUTHOR]

Abstract: The automobile industry in China has rapidly developed in recent years which resulted in an increase in gasoline usage and greenhouse gas (GHG) emissions. Focus on climate change has also accelerated to grow pressure on reducing vehicle weight and improving fuel efficiency. Aluminum (Al) as a light metal has demonstrated a great potential for weight savings in applications such as engine blocks, cylinder heads, wheels, hoods, tailgates etc. However, primary Al production requires intensive energy and the cost of Al is more than traditional steel, which may affect the total benefits realized from using Al in automobiles. Therefore, it is very essential to conduct a study to quantify the life cycle GHG emissions and energy consumption if the plan is to achieve fleet-wide Al intensive vehicles. This paper describes a life cycle assessment (LCA) methodology and the general modeling assumptions used to evaluate the impact of Al intensive vehicle on GHG emissions and energy consumption. The results indicated that the reductions in life cycle GHG emissions and energy consumption were not significant when the maximum Al content in an automobile is 145 kg, which is the average level of Al usage in automobiles in North America. A neural network methodology was used to forecast the vehicle stock in China from 2010 to 2020 and a vehicle fleet model was established to track GHG emissions and energy consumption of the vehicle fleet. A material availability factor was also introduced into the LCA methodology to further assist decision makers in providing rational proposals for a widespread implementation of Al in automobiles. A sensitivity analysis was also conducted to study the impact of the Al content in a vehicle on the final outcomes. The GHG emissions and energy consumption could be further reduced when the Al content in an automobile increases. [ABSTRACT FROM AUTHOR]

Abstract: This paper identifies the main features of CO2 emission from fossil energy combustion in China. Then it estimates China''s future energy requirements and projects its CO2 emission from 2010 to 2020 based on the scenario analysis approach. China''s rate of carbon productivity growth is estimated to be 5.4% in the period 2005–2020, while the CO2 intensity of GDP will reduce by about 50% but CO2 emission in 2020 will still be about 40% higher than prevailing in 2005 because of rapid growth of GDP. This estimation is based on the assumption that China will implement a sustainable development strategy in consideration of climate change issues. The main objectives of the strategy are to implement an “energy conservation first” strategy, to develop renewable energy and advanced nuclear technology actively, to readjust the country''s economic structure, and to formulate and legislate laws and regulations, and to build institutions for energy conservation and development of renewable energy. It concludes that international measures to mitigate CO2 emission will limit world fossil fuel consumption. China is not placed to replicate the modernization model adopted by developed countries and has to coordinate economic development and carbon dioxide emission control while still in the process of industrialization and modernization. China has to evolve a low carbon industrialization model. This is the key to the success of sustainable development initiatives in China. [Copyright &y& Elsevier]

Abstract: China''s carbon dioxide emissions from fossil fuel combustion had increased with an annual growth rate of 4.36% since 1980, hitting 1GtC in 2003. The global climate change issue is becoming more and more important and hence to be the fourth challenge for China''s future energy development, following energy supply shortages, energy security, and local environmental protection. This paper used three MARKAL (MARKet ALlocation) family models, that is, MARKAL, MARKAL-ED (MARKAL with elastic demand), and MARKAL-MACRO, to study China energy system''s carbon mitigation strategies and corresponding impacts on the economy. The models’ structures and the economic feedback formulations used in MARKAL-MACRO and MARKAL-ED are briefly described. The endogenous demands in MARKAL-MACRO and MARKAL-ED enable them to partly satisfy carbon abatement constraints via energy service demand reductions, and the reduction levels for the 30 demand sectors from these two kinds of models for given carbon emission constraints are presented and compared. The impact of carbon mitigation on social welfare from MARKAL and MARKAL-ED, and on GDP, investment and consumption from MARKAL-MACRO are evaluated. The changes in both final and primary energy mix, changes in technology development, as well as marginal abatement costs for given carbon constraints from the three models, are analyzed. [Copyright &y& Elsevier]

Good knowledge of future wind energy resources is crucial for sitting and planning studies of wind farms. The simulation results from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a proposed new downscaling method based on the bidirectional gated recurrent unit (BiGRU) are both used in this paper to study future offshore wind energy resources in China. The proposed new downscaling method is validated and compared to two traditional methods. It is found that the spatial patterns of downscaled wind speed are highly consistent with the reference data, and biases are significantly reduced by the new method, especially in coastal and shallow water areas. Using the new method, we downscale the CMIP6 future projected simulation results and generate a new dataset of offshore wind speeds in China for the period of 2021–2100 with a resolution of 0.25°. Multi-model ensemble (MME) results project small decreases in the offshore wind speed and wind power density over the East China Sea and increases in those parameters over the South China Sea, for the middle and end of the 21st Century (2041–2060 and 2081–2100) under two representative scenarios (SSP2-4.5 and SSP5-8.5). • The first work on projection of future offshore wind energy resources in China. • A new downscaling method based on deep learning network was developed. • Global climate models' simulations were downscaled to high-resolution grids of 0.25°. • Small changes in future offshore wind speed and power density were projected in China. [ABSTRACT FROM AUTHOR]