Environment, Development and Sustainability最新文献

PM_2.5, as a major air pollutant, remains unclear as to what factors influence it and the magnitude of the influence. Ten influencing factors, including socioeconomic, natural and landscape indicators, were chosen, and the effects of these factors on PM_2.5 concentration was examined through Pearson correlation analysis and the boosted regression tree model. The findings indicate that PM_2.5 concentration was most affected by GDP, NDVI and precipitation. The GDP imposed the most notable positive effect in China. The temperature imposed the greatest negative effect in East China. Northeast, North and Northwest China were the most negatively affected by the NDVI. Southwest and South-Central China were the most negatively affected by the relative humidity. More than half of the areas were affected by the main positive effects of GDP and more than a third of the areas were affected by the main negative effects of RH. This study systematically studied the correlations between PM_2.5 concentrations and their influencing factors from a spatial perspective over a long time series. The findings could contribute to a more comprehensive understanding of the factors influencing PM_2.5 and offer a theoretical basis for zonal PM_2.5 pollution management.

As a result of economic development, population growth, accelerated urbanization and the frequent occurrence of extreme weather events, the contradiction between the supply and demand for water resources between regions has become increasingly acute. In order to solve the problem of regional water shortage and irrational utilization, the optimal allocation of water resources has become one of the research hotspots in recent years. In this study, firstly a multi-objective integrated allocation model of regional water resources is constructed by introducing social, economic, and environmental objective functions to address the complex uncertainties in the water resources system. Secondly, the standard whale algorithm is optimized and improved by introducing chaotic population initialization, chaotic convergence factor, adaptive Lévy flight and improved positive cosine mechanism. The model parameters, including the 2025 water resource demand and supply, pollutant discharge content, and unit water supply cost coefficients, are set by consulting the Shanxi Water Resources Bulletin 2022, the Shanxi Provincial Department of Water Resources, and the Report on the Work of the Shanxi Provincial Government 2023. Subsequently, the improved whale algorithm is utilized for the optimization of the predicted water resources for various target years in the future in the lower reaches of the Fen River in Shanxi Province, China. This ultimately yields optimized allocation results independently from both supply and demand sides. The experimental results demonstrate that the framework for water resource optimization using the improved whale algorithm is feasible, providing a reference scheme for regional multi-objective water resource optimization. Finally, the proposed policy recommendations emphasize the necessity of strengthening water diversion planning and management, promoting virtual water and water-saving initiatives, and highlighting water recycling and environmental protection in order to ensure the sustainable allocation of water resources in the downstream Fen River basin.

Estimating crop evapotranspiration is critical in effective planning and management of irrigation systems. Accurate estimation of crop water use may aid in achieving economic and environmental benefits by optimizing water resources. Thus, the current investigation aimed to quantify crop evapotranspiration (ET_c) & develop single (K_c) and dual crop coefficients for Rabi broccoli. Thus, a 2 years field study was conducted during Rabi 2016–17 and 2017–18 by employing weighing type field lysimeter in a semi-arid climatic condition. Results revealed that, seasonal broccoli ET_c was 149 mm (mm) and 144 mm during 2016–2017 and 2017–2018 crop periods, respectively. The K_c values during Rabi 2016–17 were 0.68, 0.84, 1.04, and 0.94 for initial stage (IS), development stage (DS), mid-season stage (MS), and late season stage (LS), respectively. Similarly, in 2017–18, the K_c was found to be 0.67, 0.82, 1.03, and 0.93 during IS, DS, MS, and LS, respectively. In dual crop coefficient approach, the basal crop coefficient (K_cb) was 0.30, 0.66, 0.97 and 0.84 and the soil evaporation coefficient (K_e) was 0.42, 0.23, 0.18 and 0.23 for IS, DS, MS, and LS, respectively. While, during Rabi 2017–18, the K_cb was 0.30, 0.65, 0.98 and 0.85 and K_e found to be 0.41, 0.23, 0.18 and 0.22 for IS, DS, MS and LS, respectively. Study found that the developed crop coefficients were lesser than the food and agriculture organization (FAO) values and thus the precise crop water estimation can be possible utilizing the developed coefficients in the semi-arid climate. The developed regional broccoli crop coefficients may be utilized to improve water productivity in semi-arid environments through meticulous irrigation scheduling. The developed technological knowledge would lead to saving of precious irrigation water with optimal crop production and have environmental benefits by reducing the irrigation water need.

Graphical abstract

While rural revitalization strategies dominate public and scientific discourses, considerably less attention has been given to the key barriers that derail its full realization at the national level. This paper explores China’s rural revitalization pathway by evaluating the strategy’s four foundational pillars, and the key barriers that mar its success through expert judgement, a multicriteria decision analysis approach (i.e., Analytic Hierarchy Process), and review of existing literature. Findings put forth the following points (1) Key barriers influencing the full realization of China’s rural revitalization efforts include an ageing-population and labor shortage, income disparities between urban and rural areas, limited infrastructure and access to basic services, limited participation in governance processes, and land degradation/environmental issues (2) The critically weighted measures, highlighted by experts encapsulate support for rural industries, improved access to social services, balancing rural and urban development, land reform, and innovation (3) To revitalize rural China, ‘smart shrinkage’ is critical (4) In positive light, 'rural shrinkage' offers both direct and indirect benefits (5) The '4R value strategy' introduced presents a clear path for innovative decision- and place-making processes in rural settings. Standpoints promote equitable development and reduction of regional disparities through targeted interventions to address the key barriers identified.

To cope with risks of increasing climate changes and curb carbon emission, various policies have been implemented to facilitate energy transition in China. However, it remains unclear that whether the cash flow of energy enterprises is affected by energy transition policy and whether they invest more on R&D activities to transition. To answer these questions, we utilize a difference-in-differences method to detect the impact of the Peaking-Carbon-Dioxide-Emissions policy on the cash flow uncertainty of the energy enterprises and examine the interaction between it and R&D expenditures by using the sample of listed energy enterprises in China during 2008–2021. We find that the energy transition policy has a positive effect on the cash flow uncertainty of the energy enterprises, and the higher cash flow uncertainty after the policy further decreases the R&D expenditure of the energy enterprises. We also find that this negative role of the cash flow uncertainty is partially conducted by the reduction proportion of the long-term loan. In addition, the over-valued enterprises have stronger incentives to squeeze the expenses of R&D activities. Last, we capture the heterogeneity that the energy enterprises with less political connections and in more developed areas prefer prudent strategy management to maintain their investments in R&D activities.

Steel slags and used motor oil (UMO) are hazardous materials that are produced in high amounts and released into the environment by human activity. They adversely affect all types of life especially if they contaminate soil or ground when dumped. This research introduces a green clean safe sustainable efficient cheap method for recycling these materials. In the first step in this research, hazardous UMO is recycled as a concrete admixture. Previous research on UMO showed that the use of oil would result in higher air content in concrete, which in turn would result in a decrease in concrete strength. The research eliminates this problem by ensuring correct mixing procedures which will get rid of the increased air content. Hence, concrete strength can be maintained in the structure. The research suggests two simple economic methods to solve the problem. The first is to use an alternative mixing method of concrete to use UMO as a plasticizer without affecting the strength. Enhancement of the strength of concrete was attained by optimizing mixing time. The second method is to recycle steel slag aggregate (SSA) in concrete as coarse aggregate by replacing a certain proportion of natural coarse aggregate. The green use of SSA in concrete was beneficial in improving the properties of concrete containing UMO and was a sustainable solution for reducing the destruction of the environment caused by the depletion of natural resources when natural coarse aggregate is used. The change in mixing method and the use of SSA allowed the recycling of higher amounts of UMO without adversely affecting the fresh, hardened, or transitional plastic properties. Three states of concrete were studied in the research: fresh, plastic transitional, and hardened states. The study showed that the slump test alone is not sufficient to describe the workability of concrete containing UMO. In the transitional plastic state, setting time, bleeding, and finishability were studied. Results show that UMO and SSA can safely be used in concrete without adversely affecting its properties. The initial and final setting times increased by an average of 11 and 22 min respectively. Finishability tests show that the surface smoothness was excellent for UMO mixes containing 1.5% for SSA concretes and 1% for SSA-free mixes. The use of the modified mixing method reduced air content by about 3%. The use of SSA and the modified mixing improved the strength reduction of UMO concretes allowing dosages of about 0.8% to be safely used without losing compressive strength and 1.25% without losing tensile strength. No shrinkage problems are observed in all mixes. Lastly, a special study showed that the combined use will also reduce the overall material direct costs of about 5$ per cubic meter of concrete. Recycling UMO by this method would eliminate the CO₂ emissions produced by the firms that recycle UMO.

With the progressive realization of fuel-based energies replaced by renewable energies, the electricity generation based on 100% renewable energy will eventually achieve in the future. Therefore, before realization of the 100% renewable electricity generation, it is of importance to analyze and optimize the renewable energy system mix in advance. Via power footprint, the mathematical models used for evaluation of the 100% renewable electricity generation in each province/municipality of China towards satisfaction of the building energy requirement were created. The proposed mathematical models can be applied to evaluate the utilization magnitudes and utilization proportions of target energies, in any given year before or the year that the 100% renewable electricity generation is realized. And, based on the proposed mathematical models, the energy transfer trace (i.e., power footprint) from renewable energies to electricity used in the buildings can be able to be explored. Moreover, the optimum exploitation and utilization methods for hydro energy, wind energy and solar energy were proposed in this work, aiming at the energy conversion efficiency improvement. The research results are expected to provide the policy makers with some useful guidelines and references.

Considering the certifications for family farming, several types of certification are used in Latin America, focusing on social and environmental issues. This study seeks to analyze the social, environmental, and health certifications in Mexico and Brazil. The study contemplates a qualitative approach, using as a technique a systematic literature review on the effectiveness of agricultural private certification in developing countries (Brazil and Mexico) published between 1990 and 2016. The research intended to survey existing certifications considering categories that had not been addressed in the literature. Responsible production, and social justice factors were taken into account and it was important to have as a parameter the main certifications already existing in the Latin American context. This survey also assessed the certifications concerning their alignment with the UN Sustainable Development Goals (SDG). As a result of the research, it was possible to evaluate the social and environmental certifications for agriculture applied in Brazil and Mexico. After assessing the certifications, a framework of guidelines for certification for family farming in Latin America was structured, supported by the UN SDGs, and managed by universities. This proposal assists in achieving the SDGs proposed by the UN, mainly contemplating SDG 2 “Sustainable Agriculture” and SDG 12 “Responsible Production and Consumption”, and will also allow certifying products in a cheaper way for farmers. As future implications, the study assists in mapping existing certifications that serve as a guide for the use of producers.

The global commitment to carbon neutrality signals the decline of the traditional development paradigm since the Industrial Revolution and the emergence of a new green development model. This study analyzes data from Chinese listed companies between 2011 and 2022 to explore the impact of digital transformation on corporate green innovation.Our findings reveal that higher levels of digital transformation significantly enhance corporate green innovation. This influence operates through mechanisms such as improved corporate information transparency, better internal control quality, and increased absorptive capacity. Notably, the green innovation benefits of digital transformation are more pronounced in state-owned enterprises and heavily polluting industries compared to non-state-owned and less polluting industries. Moreover, digital transformation leads to substantial environmental benefits, particularly by significantly reducing corporate carbon emissions over time. These conclusions provide empirical evidence for assessing the green innovation effects of digital transformation and for developing targeted digital empowerment policies to support corporate green and low-carbon transitions.