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Due to the serious problems with energy efficiency, carbon emissions, and thermal comfort of rural residences in northern China, an optimization of active and passive heating technologies for rural residences is necessary. In this paper, an optimization for rural residences in northern China is conducted with four objectives: the whole life cycle carbon emission; the annual energy consumption through heating, ventilation, and air conditioning systems; the annual cost; and thermal comfort. In addition, the optimization model with active–passive heating technology synergy is resolved by NSGA-II genetic algorithm. The active and passive design variables, including the type of air source heat pump, orientation, the type and thickness of envelope insulation, the layer of window glass, the window-to-wall area ratio, as well as sunspace parameters are preferred to obtain the optimal solution. The results indicate that the optimal solution obtained by the ideal point method gives the most outstanding performance. Compared with the prototype, the optimized carbon emissions in severe cold and cold regions decreased by 56.1% and 54.6%, respectively. The annual energy consumption decreased by 59.7% and 62.2%. Finally, the roof insulation thickness is the most sensitive design variable in Pareto-optimal solution sets. This paper offers significant guidance in the application of the optimization method of active–passive technology synergy to the energy-saving design of buildings. [ABSTRACT FROM AUTHOR]

Agricultural mechanization is an important factor to improve the green total factor productivity of the planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of the output-oriented distance function to measure the green total factor productivity of China's planting industry based on net carbon sinks, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China's planting industry. The main findings of this paper are as follows: (1) Agricultural mechanization can promote the planting green total factor productivity significantly, and this basic conclusion is still robust after using instrumental variables and sub sample regression. (2) The path of agricultural mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover, while the mechanisms of operation scale expansion, factor allocation optimization and technical efficiency change are not significant. (3) With the improvement in the mechanization level, the promotion effect of mechanization on planting GTFP will become clearer. Given these findings, the paper adds considerable value to the empirical literature and provides various policy and practical implications. [ABSTRACT FROM AUTHOR]

Despite a gradual decline in rural population due to urbanization, as of 2022, approximately 35% of China's total population still resides in villages. Over a span of 40 years, carbon emissions from villages have significantly surged, with a sevenfold increase from energy consumption and a 46% rise from agriculture. Consequentially, the development of low-carbon villages is imperative. A comprehensive understanding of the primary sources of carbon emissions in villages is crucial for implementing practical and effective strategies towards low-carbon development. However, limited research has been conducted on quantifying carbon emissions and sinks for Chinese villages. This study aims to address this gap by proposing a methodology for assessing carbon emissions in villages, including the emissions of CO2, CH4 and N2O. Inspired by the IPCC standard methodology for greenhouse gas emissions at national levels and provincial greenhouse gas inventory guidelines customized for China's context incorporating localized characteristics, this approach has been applied to seven villages in Northern China based on field investigations. Employing a range of methods including field surveys, questionnaires, statistical records and big-data platforms, we collected the carbon emission activity levels of the seven villages using the most up-to-date carbon emission factors. Subsequently, the collected data and facts are quantitatively processed to generate results that are compared among the seven villages. These findings are also compared with those from other studies. The analysis indicates that the primary industries in these villages significantly influence the total carbon emissions. Moreover, the study reveals that energy consumption in buildings, agriculture, transportation and waste disposal are the most influential emission sources. These findings provide valuable insights into the carbon emission landscape of villages and can serve as a guide for implementing strategies and policies aimed at promoting low-carbon development in the rural areas of Northern China. [ABSTRACT FROM AUTHOR]

To study the impact of the promotion of electric vehicles on carbon emissions in China, the full life carbon emissions of electric vehicles are studied on the basis of considering such factors as vehicle weight and grid mix composition, and fuel vehicles are added for comparison. In this paper, we collect data for 34 domestic electric vehicles, and linear regression analysis is used to model the relationship between vehicle weight and energy consumption. Then, a Hybrid Life Cycle Assessment method is used to establish the life cycle carbon emission calculation model for electric vehicles and fuel vehicles. Finally, the life cycle carbon emissions of electric vehicles and fuel vehicles under different electrical energy structures are discussed using scenario analysis. The results show that under the current grid mix composition in China, the carbon emissions of electric vehicles of the same vehicle weight class are 24% to 31% higher than that of fuel vehicles. As the proportion of clean energy in the grid mix composition increases, the advantages of electric vehicles to reduce carbon emissions will gradually emerge. [ABSTRACT FROM AUTHOR]

China has committed to ambitious targets to reduce its carbon emissions in the next decades, in order to combat climate change and improve the environment. The realization of the targets depends on the fair and effective mitigation plans of all provinces. However, with varying ecological and environmental conditions and social-economic development, it is a critical issue to quantify the provinces' efforts equally. This paper proposed a comprehensive fE index in coordinating ecology, equity and economy, by accounting for carbon emissions and sinks to characterize provincial carbon emission status in China, from 2000 to 2017, which shows a spatial pattern of "boundary high, central low". The provinces with higher fE value (>1.5) in boundary areas can be seen as "relative equality" provinces with good ecology circulation, equity and economic efficiency. The provinces with lower fE value (<0.7) in central areas around Bohai Bay are regarded as "severe inequality" provinces, and are identified as the hot-spot provinces, which have emitted more CO2 than their equity share by occupying the carbon emission space of other provinces in recent decades. These results could provide a reference for a provincial guide for carbon reduction and sustainable development of the low-carbon economy. [ABSTRACT FROM AUTHOR]

The Cretaceous Era has always been a focus of geologic and palaeoenvironmental studies. Previous researchers believed that the impact of the global carbon cycle represents significant short-term global biogeochemical fluctuations, leading to the formation of a large number of organic rich sediments in the marine environment. During the Turonian, a large number of organic-rich oil shales were deposited in the lakes of the Songliao Basin in the Qingshankou Formation. How the depositional environment affected the formation of oil shales in continental lakes and the characteristics of these oil shales remain controversial. In this paper, through sampling of Qingshankou Formation strata, various testing methods are used to provide a variety of new data to study the characteristics of oil shales and palaeoenvironment evolution history in the Songliao Basin. The research of the sediments in the Qingshankou Formation in the Fuyu oilfield, Songliao Basin, via result analysis revealed that the oil shales possess an excellent oil-generation potential with moderate-high total organic carbon (TOC) levels (0.58–9.43%), high hydrogen index (HI) values (265–959 mg hydrocarbons (HC)/g TOC), high extractable organic matter (EOM) levels (2.50–6.96 mg/g TOC) and high hydrocarbon fractions (48–89%). The sources of the organic matter were mainly zooplankton, red algae and higher plants (including marine organisms). The aqueous palaeoenvironment of the Qingshankou Formation was a saline water environment with a high sulfate concentration, which promoted an increase in nutrients and stratification of the water density in the lake basin. Oxygen consumption in the bottom water layer promoted the accumulation and burial of high-abundance organic matter, thus forming the high-quality oil shales in the Qingshankou Formation. The global carbon cycle, warm-humid palaeoclimate, dynamic local biogeochemical cycling and relative passive tectonism were the most likely reasons for the TOC increase and negative δ13Corg deviation. [ABSTRACT FROM AUTHOR]

Based on China's goal of achieving carbon neutrality by 2060, this study focused on its coal gasification in 2010–2019. Carbon emissions were calculated from industrial data, and an LMDt model was established to analyze the influencing factors of carbon emissions. Through scenario analysis, the paths of carbon emission reductions in the chemical industry were analyzed, and their emission reduction potential was estimated. The results showed that the carbon emissions in the chemical industry increased rapidly in 2010–2019, reaching 196 million tons in 2019. The emission structure was the most important factor in mitigating carbon emissions, and the emission intensity, industrial structure, economic development level, and labor force scale had different degrees of promotion effects, of which emission intensity was the strongest. The chemical industry can reach a carbon peak before 2030 under the three analyzed scenarios, and the emission reduction potential is the largest under the landing policy scenario. The results showed that carbon capture, usage, and storage (CCUS) technology is key for carbon emission reductions and that it is necessary to adjust the industrial structure, reduce emission intensity, and increase forest carbon sink to achieve carbon neutrality in the chemical industry. [ABSTRACT FROM AUTHOR]

This study describes the first general optimization model for complex systems with uncertain parameters and decision variables represented as intervals in CDM forestry projects. We work through a specific example of the optimization method developed for a Clean Development Mechanism (CDM) forestry project in Inner Mongolia, China. This model is designed to optimize the carbon sink capacity of the new forests, and can deal with uncertainties in the carbon sink capacity, average annual rainfall, ecological parameters, and biological characteristics of tree species. The uncertain inputs are presented in the form of intervals, as are several of the optimized output variables. Compared with the project's originally recommended scheme, the optimized model will absorb and fix between 1,142 and 885,762 tonnes of extra carbon dioxide. Moreover, the ecological and environmental benefits of the project are also raised to various extents. [ABSTRACT FROM AUTHOR]

Carbon leakage features prominently in the climate policy debate in economies implementing climate policies, especially in the EU. The imposition of carbon pricing impacts negatively the competitiveness of energy-intensive industries, inducing their relocation to countries with weaker environmental regulation. Unilateral climate policy may complement domestic emissions pricing with border carbon adjustment to reduce leakage and protect the competitiveness of domestic manufacturing. Here, we use an enhanced version of GEM-E3-FIT model to assess the macro-economic impacts when the EU unilaterally implements the EU Green Deal goals, leading to a leakage of 25% over 2020–2050. The size and composition, in terms of GHG and energy intensities, of the countries undertaking emission reductions matter for carbon leakage, which is significantly reduced when China joins the mitigation effort, as a result of its large market size and the high carbon intensity of its production. Chemicals and metals face the stronger risks for relocation to non-abating countries. The Border Carbon Adjustment can largely reduce leakage and the negative activity impacts on energy-intensive and trade-exposed industries of regulating countries, by shifting the emission reduction to non-abating countries through implicit changes in product prices. [ABSTRACT FROM AUTHOR]