There has been an increasing interest in the pollution caused by meso- and microplastics (MMPs) in terrestrial ecosystems. Mulch film was once considered to be the most important source of MMPs in the mulching cultivated soil. However, the academic community has not given sufficient scientific evidence. In this study, stratified random sampling method was used to selectively interview households in Hebei province, China (400 households, 20 villages, 5 counties). Finally, household characteristics and mulch film use behavior of 41 households were collected, and corresponding soil samples were sampled. The results showed that 1) the abundance of MMPs was 29.3 ± 33.1 items·kg⁻¹ (DW) and the particle size of MMPs was 2.95 × 10³±1.75 × 10³ μm, and the proportion of MMPs derived from Polyethylene (PE) was only 18.8%; 2) the mass of MMPs was 2.90 ± 3.72 mg kg⁻¹ (DW) and the proportion of PE MMPs was 43.75%, which has the highest mass percentage; 3) After controlling the endogenous and dummy variables, the use history of mulch film (HistMF) was found to be positively correlated to the abundance of MMPs and inversely correlated to the particle size, but nor with the mass of MMPs; 4) Regarding the heterogeneous characteristics of MMPs, including particle size, color, shape, and type, the findings found the absence of a significant correlation between HistMF and the abundance and mass of PE. In summary, mulch-derived MMPs are not the primary source of MMPs in the mulching cultivated soil in terms of abundance but probably be in terms of mass.

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃–N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

High delicate particulate matter (PM₂.₅) concentration can seriously reduce air quality, destroy the environment, and even jeopardize human health. Accordingly, accurate prediction for PM₂.₅ plays a vital role in taking precautions against upcoming air ambient pollution incidents. However, due to the disturbance of seasonal and nonlinear characteristics in the raw series, pronounced forecasts are confronted with tremendous handicaps, even though for seasonal grey prediction models in the preceding researches. A novel seasonal nonlinear grey model is initially designed to address such issues by integrating the seasonal adjustment factor, the conventional Weibull Bernoulli grey model, and the cultural algorithm, simultaneously depicting the seasonality and nonlinearity of the original data. Experimental results from PM₂.₅ forecasting of four major cities (Shanghai, Nanjing, Hangzhou, and Hefei) in the YRD validate that the proposed model can obtain more accurate predictive results and stronger robustness, in comparison with grey prediction models (SNGBM(1,1) and SGM(1,1)), conventional econometric technology (SARIMA), and machine learning methods (LSSVM and BPNN) by employing accuracy levels. Finally, the future PM₂.₅ concentration is forecasted from 2020 to 2022 using the proposed model, which provides early warning information for policy-makers to develop PM₂.₅ alleviation strategies.

The impact of surface ozone pollution on winter wheat yield is empirically estimated by considering socio-economic and weather determinants. This research is the first to use an economic framework to estimate the ozone impact, and a unique county-level panel is employed to examine the impact of the increasing surface ozone concentration on the productivity of winter wheat in China. In general, the increment of surface ozone concentration during the ozone-sensitive period of winter wheat is determined to be harmful to its yield, and a conservative reduction of ozone pollution could significantly increase China's wheat supply.

Numerous experiments have demonstrated reductions in the yields of cereal crops due to tropospheric O3, with losses of up to 25%. However, the only British econometric study on O3 impacts on winter wheat yields, found that a 10% increase in AOT40 would decrease yields by only 0.23%. An attempt is made here to reconcile these observations by developing AOT40 maps for Great Britain and matching levels with a large number of standardised trial plot wheat yields from many sites over a 13-year period. Panel estimates (repeated measures on the same plots with time) show a 0.54% decrease in yields and it is hypothesised that plant breeders may have inadvertently selected for O3 tolerance in wheat. Some support for this is provided by fumigations of cultivars of differing introduction dates. A case is made for the use of econometric as well as experimental studies in prediction of air pollution induced crop loss.

Driven by the buoyancy of economy and continuous improvement of people's living standards, residential sector has gradually become the second largest CO2 emissions source in China. Reducing the fast rising rate of CO2 emissions in this sector is essential for realizing the target of carbon emission mitigation in China. The researches on the driving factors of residential CO2 emissions have attracted scholars' attention recently, yet few studies can interpret the causality relationship between household per capita income-expenditure-CO2 emissions at national and regional levels. Based on econometric techniques and a panel data set, this paper presents an investigation of the causality relationship, which combines household per capita income, per capita expenditure and per capita CO2 emissions (hereafter referred to as PI, PE, and CE, respectively) on a national level and within three regions (namely, eastern, central, and western regions of China) from 1997 to 2014. Urban and rural areas are considered as well. The empirical results manifest a varied causality relationship in different regions. For example, PI and PE correspond to CE in eastern rural area, but this phenomenon does not occur in central rural area. In addition, urban and rural differences are displayed. There is no causality between PI and PE in western urban area, while a bidirectional causal relationship emerges in PI and PE for western rural area. Finally, this study proposes some policy implications to decrease the increase rate of household CO2 emissions in China.

Nitrogen and phosphorus loads are considered a major reason for the eutrophication of the Baltic Sea. Until now, most of the abatement has been made at point sources while the implementation of policies for nonpoint sources has not led to equally large reductions in emissions. The purpose of this study is to investigate the determinants of how nutrient abatement measures are implemented by countries in the agricultural sector of the Baltic Sea region. We investigate how goal setting, policy instrument choice, and the level of implementation is determined by characteristics of the abatement measure as well as socio-economic characteristics of the country where it is implemented. Econometric analysis of a cross-sectional data set suggests that income, institutional capacity, and economies of scope in abatement and enforcement are important determinants of policies developed and their implementation.

This study measures the energy rebound effects of Chinese energy and coal power use in Chinese energy-intensive industries by using latent class stochastic frontier models like LMDI, and other various econometric estimation approach for coal-supplying regions in China ranging between 1992 and 2018. The findings reveals that China’s coal sector’s average capacity consumption is 0.81%, with a pattern of first increasing and then decreasing, falling to 0.68% in 2016 specifically. The coal capacity operation rate concerning low as well as depleted regions is generally strong, with limited space for expansion. In 2015 and 2016, the utilization rate of coal production potential in moderate-producing areas fell about 42%. Economic development variables affect the capacity utilization levels of moderate, weak, and depleted generating regions. At the same time, the price volatility cannot induce a practical improvement in the ability utilization rate, which means that China’s coal industry is mainly un-marketized. China’s energy efficiency increased about 19.98% among 2000 and 2016, while the rapidest expansion pattern has been noted in the eastern province at 39.86%, next to central (11.71%) and western regions (9.59%). The take back impact via the renewable energy and renewable productivity channels is estimated as 12.34% and 25.40%, respectively. Therefore, the take back impact is of significant importance regarding energy preservation, as China’s cumulative renewable energy use is equal to China’s aggregate energy use. On such findings, recent research also contributed by presenting novel policy implications for key stakeholders.

There are growing concerns about environmental degradation and economic expansions in West Africa. Although there are several growth-environmental studies in Africa, there is limited empirical research exploring West African countries’ potential of benefiting from the environmental Kuznets curve (EKC) hypothesis, with the few studies on this subject reporting diverse results based on selected West African countries. To fill this gap, this study explored the relationship between economic growth and environmental degradation within the EKC framework using 16 West African countries sub-grouped into low-income countries (LICs) and lower-middle-income countries (LMICs) between 1990 and 2018. This study implemented second-generation panel econometric estimators that are robust to cross-sectional dependent and parameter heterogeneity. The empirical results revealed that the data is cross-sectionally dependent, heterogeneous, integrated of order one, 1(1), and cointegrated. Controlling for other environmental determinants, panel estimates from the Augmented Meant Group and Common Correlated Effect Mean Group estimators revealed that economic growth accelerates environmental degradation in West African countries, with a greater impact on LMICs, followed by LICs in West Africa. The results also showed that West African countries especially LMICs could benefit from the EKC hypothesis. On the other hand, growth-environmental degradation among LICs in West Africa shows a monotonous increasing relationship. We found strong evidence to support for feedback hypothesis between economic growth and environmental degradation in LMICs, LICs, and West Africa as a whole. Based on the findings, policy recommendations that consider both LMICs and LICs and West Africa as a whole were offered to policymakers.

To sustain global warming below 2 °C, carbon dioxide emission mitigation has become an extensive worldwide priority. This paper proposes a comprehensive assessment by evaluating the effects of technology transfer, human capital, and renewable energy on carbon dioxide emissions among seven different regions along with the Belt and Road Initiatives from 2008 to 2018. Based on econometric estimations, it is found that human capital, renewable energy, and technology transfer show a negative but significant association with carbon dioxide emissions, indicating that an increase in human capital, renewable energy, and technology transfer can reduce carbon dioxide emissions in the Belt and Road countries. On the other hand, we found a positive and significant relationship between carbon dioxide emissions, economic growth, and foreign direct investment (FDI), indicating that economic growth and foreign direct investment increase carbon emissions. The findings of this study reveal that the adaptation of technology transfer, renewable energy consumption, and human capital are key factors in the reduction of carbon dioxide emissions in the Belt and Road counties. Our findings provide evidence of the social advantages of investing in advanced human capital, renewable energy, and technology transfer suggesting a promising route for devoting climate change without impeding economic growth.