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The inequality in CO 2 emissions from agricultural energy consumption is a major challenge for coordinating low-carbon agricultural development across regions in China. However, the evolutionary characteristics and driving factors of inequality in China’s agricultural energy-related CO 2 emissions are poorly understood. In response, the Kaya–Theil model was adopted to examine the three potential factors influencing CO 2 emission inequality in China’s agricultural energy consumption. The results revealed that, from 1997 to 2021, agricultural energy-related CO 2 emissions per capita showed a significant upward trend, with prominent polarization and right-tailing phenomena. Overall, the inequality was on a downward trend, with the Theil index falling from 0.4109 in 1997 to 0.1957 in 2021. Meanwhile, the decomposition of the national inequality revealed that the within-group inequality declined from 0.3991 to 0.1634, which was greater than between-group inequality, based on zoning the 28 provinces into three grain production functional areas. As for the three kaya factors, the energy intensity contributed the most to the overall inequality, followed by the agricultural economic development and CO 2 emission intensity. Based on these results, this study provided some potential strategies to reduce agricultural-related CO 2 emissions.

This paper studies the dynamic impacts of financial development, human capital, and economic growth on CO2 emission intensity in China for the period 1978–2015, with a structural breakpoint in 1992, by employing an autoregressive distributed lag (ARDL) approach. The estimations show that there exists a long-run cointegration linkage among the variables, with three main findings. First, financial openness measured by net FDI inflows can significantly reduce CO2 emission intensity in both the short-term and the long-term, whereas the effects of both financial scale and financial efficiency are limited and insignificant. Second, there exists an inverted N-shaped relationship between human capital and CO2 emission intensity: improving human capital first decreases CO2 emission intensity (before 1992), then increases it in the short-run (after 1992), and, finally, lessens it in the long-run. Last, raising per capita income can also significantly lower CO2 emission intensity in the long-run. Accordingly, some policy implications are also discussed.

China has set out an ambitious target of emission abatement; that is, a 60–65% reduction in CO2 emission intensity by 2030 compared with the 2005 baseline level and emission peak realisation. This paper aimed to forecast whether China can fulfil the reduction target of CO2 emission intensity and peak by 2030 based on the historical time series data from 1990 to 2018. Four different forecasting techniques were used to improve the accuracy of the forecasting results: the autoregressive integrated moving average (ARIMA) model and three grey system-based models, including the traditional grey model (1,1), the discrete grey model (DGM) and the rolling DGM. The behaviours of these techniques were compared and validated in the forecasting comparisons. The forecasting performance of the four forecasting models was good considering the minimum mean absolute percentage error (MAPE), demonstrating MAPE values lower than 2%. ARIMA showed the best forecasting performance over the historical period with a MAPE value of 0.60%. The forecasting results of ARIMA indicate that China would not achieve sufficient reductions despite its projected emission peak of 96.3 hundred million tons by 2021. That is, the CO2 emission intensity of China will be reduced by 57.65% in 2030 compared with the 2005 levels. This reduction is lower than the government goal of 60–65%. This paper presents pragmatic recommendations for effective emission intensity reduction to ensure the achievements of the claimed policy goals.

Using data from 30 provincial-level in China during 2005–2019, this paper investigates the impact of foreign direct investment (FDI) quality characteristics on carbon emission intensity. Based on the Copeland-Taylor model, the mechanism of the impact of FDI quality characteristics on carbon emission intensity is also investigated. The key findings demonstrate that FDI quality traits considerably lower regional carbon intensity, and the result is still robust after considering the spatial correlation and using IV-2sls in consideration of endogeneity. The FDI quality characteristics mainly reduce carbon emission intensity through green technology improvement, industrial structure, and factor endowment structure optimization. Moreover, further evidence shows that there are heterogeneous effects with regard to regional and FDI quality characteristics. From the standpoint of FDI quality characteristics, this study adds to the literature on FDI and environmental pollution. Policy recommendations for China are also discussed in this paper, which can serve as a guide for other emerging nations.

Regulations for the control of air-pollutant emissions from ships within pollutant emission control areas (ECAs) have been issued for several years, but the lack of practical technologies and fundamental theory in the implementation process remains a challenge. In this study, we designed a model to calculate the nitrogen-oxide-emission intensity of ships and the sulfur content of ship fuels using theoretical deduction from the law of the conservation of mass. The reliability and availability of the derived results were empirically evaluated using measurement data for NOx, SO2, and CO2 in the exhaust gas of a demonstration ship in practice. By examining the model and the measured or registered fuel-oil-consumption rates of ships, a compliance-determination workflow for NOx-emission intensity and fuel-sulfur-content monitoring and supervision in on-voyage ships were proposed. The results showed that the ship fuel’s NOx-emission intensity and sulfur content can be evaluated by monitoring the exhaust-gas composition online and used to assist in maritime monitoring and the supervision of pollutant emissions from ships. It is recommended that uncertainties regarding sulfur content should be considered within 15% during monitoring and supervision. The established model and workflow can assist in maritime monitoring. Meanwhile, all related governments and industry-management departments are advised to actively lead the development of monitoring and supervision technology for ship-air-pollutant control in ECAs, as well as strengthening the quality management of ships’ static data.

Foreign direct investment (FDI) may have a positive effect on the level of pollution in host countries, as described by the pollution haven hypothesis (PHH). However, this kind of effect may depend on the economic conditions in host countries. In this study, we conduct research on the FDI's effect on China's CO2 emissions during the market-oriented reform. The results are as follows. Firstly, FDI directly promotes China's CO2 emissions. Secondly, with market-oriented reform, this positive effect from FDI is lowering year by year, which indicates that the market-oriented reform could alleviate the positive effect of FDI on China's CO2 emissions. Thirdly, as China's market-oriented reform was implemented gradually from experimental zones to the whole country, regional market development is uneven, and as such so is FDI's effect on local CO2 emissions. Provinces in the eastern area generally evidenced higher market development and lower CO2 emissions from FDI, while four provinces in west area evidenced both lower market development and higher CO2 emissions from FDI.

A coupled structural decomposition analysis (SDA) and sensitivity analysis approach is developed to explore the drivers of China’s CO2 emission intensity at both general and sectoral levels and from both ex-post and ex-ante perspectives. Two steps are involved—structural decomposition and sensitivity analysis. First, the popular factor decomposition method, SDA, is implemented to identify which drivers “have” made the largest contribution to emission intensity changes. Second, an emerging ex-ante approach, sensitivity analysis, is introduced to answer how and to what extent such drivers “will” influence future emission intensity at a sectoral level. Based on China’s input-output tables for 1997–2012, the empirical study provides a hotspot map of China’s energy system. (1) Direct-emission coefficient and technology coefficient are observed as the top two overall drivers. (2) For the former, reducing direct-emission coefficient in an emission-intensity sector (e.g., electricity and heat sectors) by 1% will mitigate China’s total emission intensity by at least 0.05%. (3) For the latter, future emission intensity is super-sensitive to direct transactions in emission-intensity sectors (particularly the chemical industry with elasticities up to 0.82%).

Vehicle registrations have been shown to strongly react to tax reforms aimed at reducing CO2 emissions from passengers’ cars, but are the effects equally strong for positive and negative tax changes? The literature on asymmetric reactions to price and tax changes has documented asymmetries for everyday goods but has not yet considered durables. We leverage multiple vehicle registration tax (VRT) reforms in Norway and estimate their impact on within car-model substitutions. We estimate stronger effects for cars receiving tax cuts and rebates than for those affected by tax increases. The corresponding estimated elasticity is − 1.99 for VRT decreases and 0.77 for increases. As consumers may also substitute across car models, our estimates represent a lower bound.

This study examined the relationship between Environmental Performance (EP) and Financial Performance (FP) in the European food industry. The food industry is essential for population sustenance, but the rising population and the consequent increase in food production demand have implications for climate change. The aim of this study was to determine if businesses that consume water more efficiently and have lower CO emission intensities might experience improved financial performance. Financial and environmental data were sourced from external databases and company reports, and both quantile regression and correlation analyses were conducted. The results reveal that various sectors within the food industry exhibit different linkages between Environmental Performance and Financial Performance. Furthermore, our findings indicate that water use efficiency can significantly influence financial performance, either positively or negatively, while CO emission intensity did not exhibit a definitive impact on Financial Performance.

Human-made material stocks accumulating in buildings, infrastructure, and machinery play a crucial but underappreciated role in shaping the use of material and energy resources. Building, maintaining, and in particular operating in-use stocks of materials require raw materials and energy. Material stocks create long-term path-dependencies because of their longevity. Fostering a transition toward environmentally sustainable patterns of resource use requires a more complete understanding of stock-flow relations. Here we show that about half of all materials extracted globally by humans each year are used to build up or renew in-use stocks of materials. Based on a dynamic stock-flow model, we analyze stocks, inflows, and outflows of all materials and their relation to economic growth, energy use, and CO₂ emissions from 1900 to 2010. Over this period, global material stocks increased 23-fold, reaching 792 Pg (±5%) in 2010. Despite efforts to improve recycling rates, continuous stock growth precludes closing material loops; recycling still only contributes 12% of inflows to stocks. Stocks are likely to continue to grow, driven by large infrastructure and building requirements in emerging economies. A convergence of material stocks at the level of industrial countries would lead to a fourfold increase in global stocks, and CO₂ emissions exceeding climate change goals. Reducing expected future increases of material and energy demand and greenhouse gas emissions will require decoupling of services from the stocks and flows of materials through, for example, more intensive utilization of existing stocks, longer service lifetimes, and more efficient design.