Journal of Environmental Management最新文献

In the context of the low-carbon transformation of energy-intensive enterprises, the stability risks of continuous green innovation and their transmission mechanisms within the supply chain warrant attention. Specifically, will these stability risks be transmitted along the supply chain? If so, how are they transmitted? Therefore, this study focuses on Chinese energy-intensive enterprises and their upstream and downstream companies from 2008 to 2021. It explores the transmission effects of stability risks of continuous green innovation within the supply chain and the role of environmental uncertainty. By quantifying stability risk indicators of continuous green innovation and analyzing supply chain mechanisms, the study reveals the asymmetry of risk transmission and the directional and moderating effects of environmental uncertainty. The results indicate that stability risks of continuous green innovation primarily transmit upstream in the supply chain. Mechanism variables include financing constraints, degree of green transformation, and coordination of supply relationships. Additionally, the study finds that differences in supply structure contribute to heterogeneity in the transmission effect. These findings provide theoretical support for risk management strategies of energy-intensive enterprises in green innovation. It also provides practical guidance on strengthening coordination in green innovation and optimizing supply chain structure to reduce stability risk exposure.

Although green finance policy is essential for sustainable development, its impact on green development is often underestimated. Using city-level data in China from 2009 to 2022, this study identifies the causal effect of green finance policy on green total factor productivity (GTFP) through an improved synthetic control method. The findings are as follows: First, green finance positively influences GTFP growth, and this effect increases over time. Second, the baseline results remain robust when tested using alternative estimation methods and information criteria. Third, the mechanism analysis shows that green finance policy enhances GTFP through the optimization of energy structure and technological innovation. This study provides new evidence that green finance promotes green development and contributes to addressing global climate change.

Spanning both temperate and sub-frigid zones, Northeast China boasts typical boreal forests and abundant wetland resources. Because of these attributes, the region is critically significant for global climate regulation, carbon sequestration, and biodiversity preservation. While existing research explores the ecosystem service (ESs) functions of different land cover types, a thoroughly in-depth investigation into the ESs of detailed forest and wetland types is essential. This study addresses this deficiency by combining remote sensing and deep learning techniques, employing a lightweight convolutional neural network (CNN) model and a decision tree for the large-scale classification of forests and wetlands. The ESs of various forest and wetland types-encompassing habitat quality, carbon stock, and soil retention-were assessed during two periods (2008 and 2018) in Heilongjiang Province. Key factors determinants of ESs were identified using the Geodetector tool. The results indicated an overall accuracy of 0.77 in 2008 and 0.78 in 2018 for forest type classification, and 0.88 in 2008 and 0.87 in 2018 for wetland type classification. In particular, the transition from mixed broadleaf forests to mixed coniferous-broadleaf forests dominated changes from 2008 to 2018, probably due to natural succession. Among forest types, Mongolian oak forests exhibited the highest carbon stock and soil retention capacity owing to their rapid growth and deep root systems. Mixed broadleaf forests exhibited superior habitat quality, suggesting minimal disturbance. Habitat quality, carbon stock, and soil retention were found to be significantly influenced by human activity, atmospheric quality, and topographic factors, respectively. By leveraging remote sensing and deep learning methodologies, this study offers a comprehensive analysis of forests and wetlands, elucidating the nuanced ecosystem roles of specific forest and wetland types.

Sustainable water resource management in arid and water deficit regions requires optimal use of water resources due to competition among different water sectors. The purpose of this study is to model uncertainties in economic and hydro-climatic variables and parameters to optimize agricultural water management in irrigation system networks to have sustainable water use. The study focuses on mitigating water shortages in Iran's Varamin irrigation network through improved agricultural patterns and efficient water consumption. The impact of employing reclaimed wastewater containing nitrates instead of nitrogen fertilizers is also evaluated. A Fuzzy Multi-Objective Particle Swarm Optimization (F-MOPSO) is applied to maximize the net benefit, restore groundwater, and minimize nitrate leaching into the aquifer. The results demonstrate that replacing groundwater with reclaimed wastewater boosted net benefits by 21% while improving groundwater restoration by 82%. These results indicate that the developed fuzzy model can handle uncertainties in irrigation system networks with a sustainable water use perspective. This research can assist decision-makers within the water, agriculture, and the environment in finding sustainable water solutions and improving the current water consumption practices, considering environmental aspects of nitrogen leaching in other regions and highlighting the potential of the developed fuzzy model.

Optimizing CO₂ storage efficiency in Deep saline aquifers (DSA) involves improving each storage trapping mechanism, such as structural/stratigraphy, capillary/residual, mineral, and dissolution trapping mechanisms, while maintaining the reservoir integrity for long-term carbon capture and storage (CCS). These enhancements are driven by a series of geochemical reactions that favorably modify petrophysical, mineralogy, wettability, rock geomechanics of the rock, and dissolution of CO₂ in aquifer fluid. Three different CO₂ injection strategies have been identified and tested for optimizing CO₂ storage and efficiency- Continuous CO₂ injection (CCI), Water Alternating Gas (WAG), and Simultaneous scCO₂-brine Aquifer Injection (SAI). This study investigates the effect of integrating produced water (PW) into WAG and SAI strategies for CO₂ storage, emphasizing how the salinity of the injected water affects reservoir properties alterations in sandstone and limestone formations exposed to scCO₂. Experimental results show that high salinity levels accelerate mineralogy changes and wettability alteration, particularly in limestone, leading to porosity, permeability, and mechanical strength changes. While the SAI results showed more aggressive and detrimental changes in rock properties, WAG leads to slower reaction rates, a more stable and effective strategy with more gradual alterations in rock properties due to its ability to balance fluid flow and mechanical strength, hence offering greater stability for long-term CO₂ storage. Based on these findings, a 20-50 g/L salinity range is recommended to maintain reservoir integrity and reduce the negative impacts of salinity on CO₂ storage efficiency and storage. This study provides valuable insights for optimizing CO₂ storage in DSAs, enhancing environmental sustainability, and enhancing mineral trapping through more targeted geochemical reactions and lower changes in rock mechanical strength.

In the context of global digitalization, it is of great significance to promote the development of digital economy to reduce carbon emissions and improve green total factor productivity (GTFP). Based on the data of 30 provinces in China from 2011 to 2021, this study constructs a variety of methods, such as Super-SBM model, fixed effect model and intermediary effect model, to empirically test the impact and mechanism of digital economy (DIE) on GTFP. Based on the research findings, the growth of DIE contributes to the improvement of GTFP. Moreover, the impact of DIE on GTFP in eastern China is greater than that in central and western China. There are significant differences between the development level of DIE and GTFP in eastern, central and western regions. Further research shows that digital economy affects the improvement of GTFP through three intermediary variables: economic scale, industrial structure and technological innovation. This study provides empirical evidence to support the effective enhancement of GTFP in developing countries as the digital economy evolves. It provides effective recommendations for developing countries and emerging economies to develop a green economy. However, this study has limitations in data sample, research scope and mechanism analysis depth. Therefore, the conclusions drawn in this study can only provide empirical evidence for identifying the relationship between the DIE and GTFP to a certain extent. Future research should be expanded in these aspects.

In the context of a global water crisis, enhancing water productivity is becoming increasingly crucial. While previous research has predominantly addressed technical and policy aspects of water management, the role of fintech in improving water productivity has not been sufficiently explored. This research investigates the impact of fintech on water productivity, considering the moderating effect of education level. Using panel data from new BRICS countries spanning 2011 to 2021, we employ a partially linear functional model to analyze how fintech influences water productivity and assess how education levels moderate this relationship. Our findings reveal that: (i) Fintech holds significant potential for improving water productivity; (ii) The effect of fintech on water production varies with the education level; (iii) There is considerable spatial variation in how education level affects the impact of fintech, with a more pronounced effect observed in countries with higher education levels. Specifically, the impact of fintech on water productivity becomes substantially more significant when the education level index exceeds 2.3. These results remain robust across various tests. Based on these insights, the paper proposes policy recommendations to enhance water productivity through the integration of fintech and education improvements.

Water quality monitoring data from various points within the same basin often show non-uniformity. A key scientific question is how to extract relevant knowledge from data-rich sites (source domains) and leverage the possible inter-site consistency of water quality to compensate for the limitations of data-poor sites (target domains). Transfer learning (TL) methods can improve the applicability of water quality predictions for data-poor sites but their comparison and combination have not been fully explored. This study employs feature-based (Transfer Component Analysis, TCA) and model-based (pretraining and fine-tuning) transfer learning, to assist in constructing Long Short-Term Memory (LSTM) models for forecasting the dissolved oxygen (DO) levels in the West Channel of Guangzhou, southern coastal China. The LSTM models at Yagang and Shimen stations were constructed as the basic and baseline models for source and target domains, respectively. By comparing and selecting different transfer learning strategies, the best single-type TL strategy emerged as a multi-sequence LSTM model without TCA but with the fully connected layer frozen after pretraining. It achieved increases in validation Nash efficiency coefficient (NSE) of 5.2%, 10.8%, and 46.2% for predicting DO over the next 3 days, respectively, compared to the baseline LSTM model at Shimen station. The best combined TL strategy involved using TCA and freezing the second fully connected layer in a multi-sequence LSTM model. It improved upon the baseline LSTM model with a validation NSE increase of 5.3%, 21.4%, and 48.7% over the next three days, respectively. This study demonstrates that combining feature- and model-based transfer learning methods can yield better DO prediction performance in data-poor rivers than using a single-type transfer learning method.

This study delves into the dynamics of China's environmental and trade policies and their impact on environmental sustainability over the period spanning from 1998 to 2022. Employing the autoregressive distributed lag methodology for empirical analysis, our investigation reveals distinctive patterns in policy outcomes. First and foremost, our research illuminates the affirmative role of environmental policy in fostering environmental sustainability. The observed negative effect of environmental policy on carbon dioxide emissions underscores its efficacy in mitigating environmental degradation. In contrast, our findings bring to light the counteractive influence of trade policy on environmental sustainability. The positive effect of trade policy on carbon dioxide emissions signals potential challenges emanating from heightened trade openness concerning environmental preservation. Furthermore, our study elucidates the complex interplay involving information and communication technology, financial development, and fossil energy consumption and their implications for environmental sustainability. The discernible positive effects of these factors on carbon dioxide emissions underscore the need for policy alignment with environmental sustainability objectives. In sum, our research contributes to an enhanced comprehension of the intricate relationship between policy interventions, technological facets, and environmental outcomes within the context of China. These insights bear significance for policymakers and stakeholders striving to navigate the multifaceted landscape of economic growth and ecological preservation in China. Balancing these imperatives is central to achieving lasting environmental sustainability.

Low impact development (LID) are prominent type of vegetated stormwater infrastructure that provides various ecosystem services, such as biodiversity, carbon storage, and improvement in air quality. This study investigated six LID technologies to assess SOC retention and factors influencing accumulation. Soil samples (0-20 cm depth) were analyzed using the Walkley-Black method, specifically focusing on wet oxidation. SOC stocks ranged from 18.5 to 66.3 t C/ha in the inflow and 18.6 to 79.1 t C/ha in the outflow, with SCW and TBF showing higher SOC due to root turnover, stormwater runoff, and media composition. This study found that vegetation and impervious catchments significantly influenced SOC levels. Trees exhibited higher SOC due to their extensive root systems and longer life cycles. Roads and parking lots had higher SOC from plant debris and hydrocarbons in stormwater runoff. SOC also varied seasonally, peaking in spring due to photosynthesis and decreasing in summer and autumn from increased microbial respiration. A complex relationship between SOC and soil physico-chemeical perameters were also investigated, with moisture content and total nitrogen being critical factors for carbon stocks. Overall, the results from this study are seen as beneficial in optimizing the design guidelines for LID technologies for carbon sequestration and green space expansion in urban areas.