China's economy has grown significantly and concomitantly so has the demand for home and personal care (HPC) products. The detection of chemicals used in HPC products is increasing in profile as China strives to improve its environmental management. China is developing robust exposure models for use in regulatory risk-based assessments of chemicals, including those chemicals used in HPC products. Accurate estimates of chemical emissions play an important role within this. A methodology is presented to derive spatially refined emissions from demographic and economic indicators with large variations in emissions calculated, showing product usage being higher in East and South China. The less affordable a product, the greater the influence per capita Gross Domestic Product has on the product distribution. Lastly, more spatially resolved input data highlights greater variation of product use. Linking product sales data with population density increased the observed variability in absolute usage distribution of HPC products at the county > province > regional > country scale.

Following formative work in the 1970s, disappearance in the 1980s, and reemergence in the 1990s, a chronological review shows that the past decade has witnessed increasing interest in the study of urban metabolism. The review finds that there are two related, non-conflicting, schools of urban metabolism: one following Odum describes metabolism in terms of energy equivalents; while the second more broadly expresses a city’s flows of water, materials and nutrients in terms of mass fluxes. Four example applications of urban metabolism studies are discussed: urban sustainability indicators; inputs to urban greenhouse gas emissions calculation; mathematical models of urban metabolism for policy analysis; and as a basis for sustainable urban design. Future directions include fuller integration of social, health and economic indicators into the urban metabolism framework, while tackling the great sustainability challenge of reconstructing cities.

Air pollution by particulate matter is well linked with anthropogenic activities; the global economic crisis that broke out in the last year may be a proper indicator of this close relationship. Some economic indicators show the regional effects of the crisis on the Cantabria Region. The present work aims to evaluate the impact of the economic crisis on PM10 levels and composition at the major city of the region, Santander. Some metals linked to anthropogenic activities were measured at Santander and studied by Positive Matrix Factorization; this statistical analysis allowed to identify three main factors: urban background, industrial and molybdenum-related factor. The main results show that the temporal trend of the levels of the industrial tracers found in the present study are well agree with the evolution of the studied economic indicators; nevertheless, the urban background tracers and PM10 concentration levels are not well correlated with the studied economic indicators.

Over the last few years, intensive agriculture has often been denounced as a source of negative effects, particularly at the environmental and health level (overexploitation of natural resources, degradation of their quality, appearance and development of several diseases, etc.). Reducing the excessive use of agricultural inputs for the protection of the environment and the preservation of human and animal health is a social requirement nowadays. Investing in more sustainable agricultural models which make it possible to reduce, or even eliminate the risks, has become urgent. A possible solution may be to resort to agroecological systems. In order to be sustainable, these new systems must be performant at the agronomic, economic, social, and environmental levels. There is a multitude of tools for assessing the sustainability of agricultural systems. These tools are inappropriate for organic and agroecological systems, and do not make it possible to measure the agroecological transition performance of farms (Trabelsi et al. Environ Sci Pollut Res 23:139–156, 2016; Trabelsi 2017). This research project aims to design a decision support tool in order to help farms throughout the agroecological transition process, to assess the performance of this transition, and to put forward improvement scenarios. Contrary to other assessment methods, ESSIMAGE (Evaluation and Simulation of Agroecological Systems) is based on both pressure and impact indicators, and takes the specificities of agricultural production systems into account. It is a dynamic tool which not only makes it possible to assess farm performance at the present moment but also to consider the future by putting forward possible alternative improvement scenarios and by simulating their consequences at a later stage. ESSIMAGE is based on the interaction of two elements: agro-environmental, social, and economic indicators, and the GIS (Geographic Information System) software. This tool has been tested as part of a CASDAR “Post-MAET Gimone” (agriculture.gouv.fr/ministere/mobilisation-collective-pour-lagroecologie.) project on the subject of “Collective mobilization for agroecology” by using farm data, most of the farms having been involved in an agro-environmental measure for the progressive reduction of phytosanitary treatments since 2008. It has made it possible to compare the agroecological performances of these farms with an optimal situation, as well as with each other. Considering the research objectives and the approaches discussed, this study is an original step in the development of agricultural management strategies in favor of agroecology.

The main objective of this study is to examine the environmental Kuznets curve (EKC) hypothesis by utilizing the ecological footprint as an environment indicator and GDP from tourism as the economic indicator. To achieve this goal, an environmental degradation model is established during the period of 1988–2008 for 144 countries. The results from the time series generalized method of moments (GMM) and the system panel GMM revealed that the number of countries that have a negative relationship between the ecological footprint and its determinants (GDP growth from tourism, energy consumption, trade openness, and urbanization) is more existent in the upper middle- and high-income countries. Moreover, the EKC hypothesis is more present in the upper middle- and high-income countries than the other income countries. From the outcome of this research, a number of policy recommendations were provided for the investigated countries.

This study investigates the relationship between rural non-point source (NPS) pollution and economic development in the Three Gorges Reservoir Area (TGRA) by using the Environmental Kuznets Curve (EKC) hypothesis for the first time. Five types of pollution indicators, namely, fertilizer input density (FD), pesticide input density (PD), agricultural film input density (AD), grain residues impact (GI), and livestock manure impact (MI), were selected as rural NPS pollutant variables. Rural net income per capita was used as the indicator of economic development. Pollution load was generated by agricultural inputs (consumption of fertilizer, pesticide, and agricultural film) and economic growth with invert U-shaped features. The predicted turning points for FD, PD, and AD were at rural net income per capita levels of 6167.64, 6205.02, and 4955.29 CNY, respectively, which were all surpassed. However, the features between agricultural waste outputs (grain residues and livestock manure) and economic growth were inconsistent with the EKC hypothesis, which reflected the current trends of agricultural economic structure in the TGRA. Given that several other factors aside from economic development level could influence the pollutant generation in rural NPS, a further examination with long-run data support should be performed to understand the relationship between rural NPS pollution and income level.

This paper investigates the nexus between carbon emissions (CO₂) and economic growth in West Africa based on the Environment Kuznets Curve (EKC) hypothesis by utilizing spatial panel data technique to check the possible effect of spatial dependence among countries in West Africa. Our empirical findings suggest the presence of spatial dependence of carbon emissions distribution in West Africa. By examining the existence of EKC embedded within the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) approach, we conclude an inverse N-trajectory of the relationship between carbon emissions and economic growth. Furthermore, to mitigate global carbon emissions, we utilize a recurrent neural network (RNN) bidirectional long short-term memory (BiLSTM) algorithm devoid of exogenous variables and assumptions to forecast carbon emissions from the year 2015 to the year 2030 based on the predictive accuracy of our formulated algorithm. Due to the upward trends in future emission levels, we propose emissions mitigation pathways for countries in West Africa to still hold carbon emissions-related global warming well below 1.5 and 2 °C. Such mitigation pathways proposed could help implement strategic policies to minimize carbon emissions to a considerable level. As a policy implication, drafting strict environmental regulations and utilizing renewable energy technologies will help mitigate carbon emissions for all West African countries.

The re-examination of the existing economic and environmental policies in the South and Southeast Asian countries seems to be necessary, as these countries are struggling to achieve the goals of sustainable development. For designing a long-term environmental policy, we intended to examine whether the selected economic and demographic indicators have influenced the ecological footprint in the eight developing countries of Asia from 1990 to 2015. The use of pooled mean group (PMG) approach allowed driving the long-run common coefficients, which may facilitate us to develop a common policy framework for fortifying the environmental quality. The computed results confirmed that the selected variables are cointegrated in the long run, and the variables, i.e., per capita income, nonrenewable energy usage, urbanization, fertility rate, and population density are observed as the significant drivers of the environmental pollution. Moreover, the consumption of renewable energy restored the environmental quality in these countries. Based on the results, we recommended the need for the diversification of the energy-basket for enhancement of the use of renewable energy resources. Further, through sensitization of the necessity of environmental conservation, the governments should promote less carbon-intense economic and demographic practices across the industries and sectors.