Environment, Development and Sustainability最新文献

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.

Women play several significant roles in biodiversity conservation, be they linked to conservation’s main activities or decision-making. However, there are countless barriers and disparities capable of affecting the performance of their roles. From this perspective, this study aimed to investigate and compare the roles played by women in the conservation and management of biodiversity. Additionally, it sought to evaluate the obstacles and disparities they face in this process, focusing on both developed and developing countries. We carried out a systematic review of the literature, using the PRISMA Protocol to avoid biased data. We reviewed 400 articles. However, only 73 articles were included in this study, as they met the inclusion criteria. We used the Discursive Textual Analysis method to identify the categories of roles and obstacles found in the articles. We have identified 8 categories of roles played by women in biodiversity conservation and 11 obstacles/difficulties faced by them to perform their role. Developed countries did not show conservation’s main activities, only environmental governance categories, biodiversity management, and others (environmentalist movements). Concerning developing countries, ‘management of natural resources’ was the most cited category in the analyzed studies. There were several main activities related to agriculture in developing countries, mainly agroecology, sustainable agriculture, and agrobiodiversity. Developed countries stood out for double shifts, lack of public policies, lack of financing, gender discrimination, cross-sectional factors, and climatic skepticism, as obstacles to biodiversity conservation. We have concluded that there are differences in the roles played by women in developed and developing countries. We understood that the cross-sectional factors, that is, factors that encompass ethnicity, race, age, geographic location, gender, religion, sexual orientation, and/or the condition of a person with a disability, were the most observed obstacle/difficulty in both developing and developed countries.

Rural environmental governance is one of the important tasks in China’s rural revitalization strategy. Currently, relying solely on government governance cannot effectively solve the environmental governance needs of nearly 700 thousand administrative villages in China. Building a collaborative governance model of “government regulation + villagers’ supervision” is the key to achieving successful rural environmental governance. Previous literature mainly used the evolutionary game model under the static reward and punishment mechanism (SRPM) to study the strategy evolution dynamics of village enterprises, the government and villagers. However, the simple model setting makes it difficult for the game system to accurately depict complex real-world problems, and it is concluded that the evolutionary stable strategy (ESS) of the game system can only be pure strategy Nash equilibrium (PSNE). In fact, it is difficult to see the stable strategy of “either this or that”, and most of the strategy combinations with asymptotic stability are mixed strategy Nash equilibrium (MSNE) rather than PSNE, which indicates that the model construction based on the SRPM is problematic. Therefore, this paper expands the SRPM and studies the strategic evolution dynamics of village enterprises, the government and villagers under the dynamic reward and punishment mechanism (DRPM). Theoretical research has shown that MSNE may indeed be the system’s ESS under the DRPM, and a mathematical proof was provided. System simulation has shown that under the SRPM, village enterprises, the government, and villagers all exhibited a periodic strategy selection mode, and the strategies of all parties cannot achieve asymptotic stability. However, under the DRPM, the strategies of all parties eventually tend to stabilize over time, and MSNE is the system’s ESS. This indicates that there is indeed a situation where MSNE is the system’s ESS, and also confirms that the DRPM is indeed a stability improvement compared with the traditional SRPM. Finally, it is suggested that the government should focus on raising the upper limit of punishments for illegal emissions by village enterprises, and should carefully raise the upper limit of rewards for active supervision by villagers.

Microwave (MW) pyrolysis showed a promising and efficient mean of deriving energy from food waste (FW). This study evaluated the thermal decomposition characteristics of FW and commingled FW (FW mixed with low density polyethylene; LDPE (87:13)) using the thermogravimetric analyzer and the MW copyrolysis reactor. Thermograms of commingled FW (up to 892 K) using different MW susceptors (Granular Activated Carbon (GAC), Cement, Silica gel, Flyash, and Biochar) demonstrated complete devolatilization within 700–1100 s of heating time. A maximum weight reduction of 89.3 wt% was achieved for the commingle FW at 753 ± 1 K within 700 s using GAC as the MW susceptor. The MW absorptive capacity of different MW susceptors strongly influenced the thermal decomposition characteristics of FW and LDPE, and the activation energy of the MW copyrolysis; accordingly, the activation energy varied 7.01–12.03 kJ/mol with different MW susceptors. Thermal decomposition of commingled FW in MW copyrolysis could be best represented with the Kissinger–Akahira–Sunose (KAS) method (R² = 0.85–0.95). Gibbs free energy (104.60–148.15 kJ/mol), free entropy (600.520–601.662 J/mol/K), free enthalpy (1.055–6.412 kJ/mol) showed non-spontaneity, low randomness and endothermic behaviour of the process. Overall, the low activation energy of the MW copyrolysis process (7.01 kJ/mol) achieved with the biochar as the MW susceptor showed a promising future for MW copyrolysis in developing efficient, environmental-friendly and sustainable conversion technology for commingled FW processing.

An experiment was conducted in 2018 to investigate the effect of polyamine and biochar treatments on physiological traits of garlic under saline conditions. Salinity increased the activities of the enzymes (2.38-166.66%), 2,2-diphenyl-1-picrylhydrazyl (DPPH) (3.72–8.32%) and 2,2^′-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) (7.88–9.85%) radical scavenging activity, malondialdehyde (MDA) (32-59.15%), proline (21.39–45.29%) and soluble sugars contents (35.58–71.67%), ion leakage (22.95–62.01%) and also leaf temperature (LT) (13.18–39.37), but decreased leaf water content (LWC) (2.17–14.90%), chlorophylls (Chl a (32–45%), Chl b (26–54%) and chlorophyll index (CCI)) contents (10.67–21.78%), chlorophyll fluorescence (Fv/Fm) (9.06–16.44%) and total phenolic concentration (33.19–64.24%). Application of biochar and polyamines decreased LT, MDA and proline contents, ion leakage, soluble sugars and enzymes activities, but increased the Chl a, Chl b and CCI contents, Fv/Fm and total phenolic concentration. Also, application of biochar enhanced the LWC (1.97–3.88%) and carotenoid (6.23–14.19%) contents. Climate change had caused many threats soil ecosystem, among them, soil salinity. Salinity is one of the widespread and main challenges in the recent era that hinders environmental sustainability and global food security. Thus several strategies are suggested to mitigate this issue. In this context, biochar and polyamines are known as potent amendments able to alleviate the salt stress on the crops. Application of biochar and polyamines alleviated the harmful effects of soil salinity on physiological performance of plants such as garlic and also application of putrescine and 20% of biochar were superior treatments compared to other treatments. Our findings suggest a valuable starting point for developing crop management strategies based on biochar and polyamine applications to enhance plant performance under saline conditions and reduce freshwater dependence in agriculture.

Enhancing the management of working capital in supply chains due to fluctuations in demand necessitates the utilization of financial resources such as loans and trade credit. Small and medium-sized enterprises in developing countries often face financial challenges and lack the necessary credit history to secure bank loans. Consequently, trade credit has emerged as a viable debt-based financing alternative. This article presents a two-objective mathematical model for a three-level, multi-period, multi-product supply chain network, in which suppliers provide trade credit to plants for raw material procurement. Furthermore, plants offer trade credit to distribution centers, a novel approach absent from previous studies. The primary objective is to maximize the net present value of shareholders' wealth at the end of the planning horizon, while the secondary objective focuses on maximizing the fill rate. The AEC method and CPLEX solver were employed to solve the model in small dimensions. Given the model's categorization as NP-hard, the nondominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization metaheuristic algorithms were utilized for solving the model in large dimensions. Additionally, the model's validity was investigated through real-world applications.

The study aims to identify the determinants of the transition of rural households from dirty to mixed fuels instead of clean fuels as energy sources in India. Cleaner energy choices are critical to sustainable economic development, improved public health, and addressing environmental concerns, especially among rural households. Understanding the key factors that lead to the transition from dirty to mixed fuel instead of clean fuel is imperative for policymakers to promote cleaner energy choices in rural areas. To aid this understanding, the current study uses a balanced panel constructed from the data of the Indian Human Development Survey for the years 2005 and 2012. A fuel choice transition matrix is then constructed to analyze the energy transition at the household level. Finally, the multinomial logit model is employed to reveal the key factors that affect the change in energy usage among households using dirty fuels in 2005 to their status in 2012-persistent use of dirty fuel, shift to mixed fuels, or adoption of clean fuels. The findings confirm that apart from income, the level of education and gender of household heads play a vital role in the transition of energy sources. This research emphasizes the need for targeted policies addressing the diverse socio-economic and educational barriers obstructing the shift to cleaner fuels in rural areas.

Graphical abstract

Spatial prediction of debris flows in terms of susceptibility mapping is the first and foremost requirement for disaster mitigation. In the present study, a comparative evaluation of machine learning and statistical approaches for debris flow susceptibility zonation (DFSZ) mapping has been attempted using 10 causative thematic layers (slope, aspect, elevation, plan curvature, profile curvature, topographic wetness index, stream power index, geology, proximity to streams, normalized difference vegetation index) and a debris flow inventory containing 85 debris flow locations. The employed machine learning (ML) approaches include random forest (RF), naïve Bayes (NB), and extreme gradient boosting (XGBoost) models whereas statistical models include the weight of evidence (WoE) and index of entropy (IoE). The results indicated that in all 5 DFSZ maps, about 21.20–47.98% of the area is very highly and highly susceptible to debris flows. It is observed that the major debris flows as well as high susceptible zones are distributed along the river Alakananda and its tributaries and at the vicinity of the NH-58. Among the statistical models, the DFSZ map prepared using the weight of evidence (WoE) model provides higher accuracy in terms of the success rate and the prediction rate compared to that prepared using the index of entropy model (IoE). Among the machine learning-based models, both the extreme gradient boosting (XGBoost) and random forest (RF) models give better accuracy and are more efficient than the Naïve Bayes (NB) model. It is also observed that the ML models perform better than the statistical models for a part of Chamoli district, Uttarakhand state (India). The novelty of the present study lies in the spatial prediction of one of the most destructive forms of mass movement (debris flow) in the Indian Himalayas using statistical and ML models and establishing the superiority of the ML Random Forest & XGBoost model over other ML and statistical models for the present case. This study will help make decisions on the suitability of developmental activities and human settlement in the area under consideration. The present study is one among the few studies focused on the DFSZ mapping in Indian Himalayas and can be replicated in other debris flow prone regions worldwide.

The widespread use of coal as a primary source of commercial energy in India resulting in substantial waste production from power plants, including fly ash and bottom ash. Inappropriate disposal of these waste by-products poses a range of environmental challenges and hence requires proper attention. The current work examines the physico-chemical nature of coal and ash characteristics of the power plant in Rupnagar, India. A combined approach using X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy, Fourier Transform Infrared (FTIR), Energy Dispersive X-ray, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was used to characterize the samples. As a result, the total moisture content of the feed coal exhibited a range of 7.09–9.75%, while the fly ash and bottom ash varied from 4.19–6.28% and 2.16–5.12%, respectively. The air-dried ash and air-dried moisture content in coal varied between 5.95–6.39% and 39.5–44.81%. The volatile matter in the feed coal samples showed variability within the range of 19.71–21.34%. The coal’s gross calorific value was measured in the range of 14.23–15.87 MJ kg⁻¹ having carbon and sulfur content of 39–43% and 0.35–0.48%, respectively. Further, XRD analysis showed quartz, mullite, kaolinite, and hematite in feed coal, fly ash, and bottom ash. Morphologically, fly ash was characterized by fine spherical particles compared to bottom ash, which were observed as large carbon particles with a high abundance of Si and Al in chemical composition. Furthermore, the fly ash samples exhibited higher concentrations of various heavy metals, particularly Zn (80.67 mg kg⁻¹), Cu (25.66 mg kg⁻¹), and Pb (16.7 mg kg⁻¹) compared to bottom ash and the feed coal. FTIR analysis showed the Al–O and Si–O due to the large kaolinite and quartz particles. By examining coal, fly ash, and bottom ash, this research aims to provide important insights into coal combustion products and reduce the environmental impact of waste generation from power plants.

Graphical abstract

The city of Chandigarh has been urbanizing and expanding at an aggressive rate. Despite the urban expansion being mostly planned in nature has shown underlying indications of deteriorating ecological health in the city and its abutting natural resources. Changing migration patterns and decreasing vegetation cover are just few of the indicators raising the need to analyse the ecological quality of the region. Ecological quality can be seen as a measure of the health of an environment to sustain life. Remote sensing can be used to monitor the land surface over varied spatiotemporal extents. This study uses the Remote Sensing-based Ecological Index (RSEI) to study the spatiotemporal changes in the environmental quality of the city of Chandigarh over four decades. Four factors i.e., greenness, wetness, dryness, and heat that affect environmental quality are analysed using principal component analysis to calculate RSEI. The contribution of each of the indicators to RSEI and the spatial correlation of results are studied using correlation analysis and Moran’s Index. Landcover maps are developed using Cart classifier to understand the growth patterns and establish relation to changes in ecological index values. The change in RSEI for individual land cover categories shows the degradation of ecological health in natural resources. The RSEI values of vegetation and surface water show a deteriorating trend from 1991 to 2020. Furthermore, the study area shows intense degradation of RSEI values in the city outskirts where a major shift to built-up landcover has taken place. The association of landcover change and its impact on ecological quality can assist planners in adopting suitable strategies to assure that ecological health is integrated when urban expansion is carried out. This study provides insights into the development strategies and their impact on the ecological resources of the city that may otherwise not be identified by overall RSEI value and landcover assessment.