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.

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.

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.

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.

Militarization is crucial for the sovereignty of a nation; however, there are many environmental hazards associated with increased military spending. Previous panel studies mainly captured the short-run effects of militarization on the environment. Limited scholars determined the long-run environmental impacts of militarization but they mostly ignored possible cross-sectional dependence and heterogeneity problems in panel data. Our research highlights this deeply neglected area and examines the impact of militarization on the environment in 22 OECD countries by controlling economic growth, renewable energy, and fossil fuel consumption. Drawing on an extensive dataset from 1971 to 2020, we employed advanced econometric approaches robust against endogeneity, heterogeneity, and cross-sectional dependence. The results of the cross-sectional augmented autoregressive distribute lag (CS-ARDL) analysis indicate a positive contribution of militarization to CO₂ emissions implying that militarization is adding to the environmental degradation in OECD nations. This evidence proves the treadmill of destruction theory for OECD nations in the modern world. Economic growth and fossil fuels consumption increase CO₂ emissions, while renewable energy mitigates emissions. Moreover, economic growth Granger causes militarization. Our results suggest that reduction in militarization level and energy conservation strategies will not hamper the economic progress of selected OECD countries.

Indeed, the Belt and Road Initiative (BRI) plays an increasingly important role in global economic and climate change mitigation. However, scientists have insufficient attention to the issues related to the elements that contribute to justifying these impacts and bolstering its response in BRI nations. Accordingly, the existent study executed an in-depth examination of the spatial direct and spillover effects of foreign direct investment inflows (FDI) and biomass energy consumption (BEC) on greenhouse gas emissions (GHG) for 57 BRI countries (1992–2012). We applied the spatial lag model (SLM), the spatial error model (SEM), and the spatial Durbin model (SDM) with five different weights matrices to verify the existence of the pollution haven hypothesis (PHH), the pollution halo hypothesis (P-HH), and the N-shaped environmental Kuznets curve (EKC). We linked the study results with the implementation level of the sustainable Development Goals (SDGs). The findings of local Moran’s I (LMI) and Lagrange Multiplier (LM) tests confirm the existence of spatial autocorrelation (SAR). The empirical results revealed that FDI has a positive direct and spillover influence on GHG emissions, which supports the presence of PHH. Also, the nexus between economic growth and GHG emission is an N-shaped curve. The results revered that BEC has a negative sign for direct and spillover effects. In contrast to BEC, Fossil Fuel Energy Consumption (FFEC) and population positively sign for direct and indirect impact. Some policy proposals and future research directions are discussed for BRI countries.

This study examined the nexus between energy consumption and environmental quality in light of China’s 2060 carbon-neutrality agenda utilizing annual frequency data from 1971 to 2018. In order to obtain valid and reliable outcomes, more robust econometric techniques were employed for the analysis. From the results, all the variables were first differenced stationary and cointegrated in the long-run. The elastic effects of the predictors on the explained variable were explored through the ARDL, FMOLS, and the DOLS techniques, and from the discoveries, energy utilization worsened environmental quality in the country via more CO₂ emissions. Also, industrialization and urbanization deteriorated the country’s environmental quality; however, technological innovations improved ecological quality in the nation. On the causal connections between the variables, a unidirectional causality from energy consumption to CO₂ effluents was discovered. Also, feedback causalities between industrialization and CO₂ secretions, and between urbanization and CO₂ exudates were disclosed. However, there was no causality between technological innovations and CO₂ emanations. Based on the findings, the study recommended among others that, since energy consumption pollutes the environment, the country should transition to the utilization of renewable energies. Also, the government should allocate more resources to the renewable energy sector. This will help increase the portion of clean energy in the country’s total energy mix. Furthermore, research and development that are linked to the utilization of green energies should be supported by the government. Data constraints were the main limitation of this exploration. Therefore, in the future, if more data become available, similar explorations could be conducted to check the robustness of our study’s outcomes.