Environment, Development and Sustainability最新文献

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.

Owing to real constraints, a first-best climate policy is rarely socioeconomically optimal; therefore, policymakers may prefer a second-best or mixed policy, where revenue recycling (RR) is usually implemented as a complementary policy to the first-best policy. Unfortunately, how different RR policies affect equality and efficiency during first-best policy implementation remains to be researched. This paper attempts to narrow the research gap by designing and evaluating the RR policies for the emission trading scheme (ETS) simulating the Chinese National Emission Trading Scheme (CNETS). To achieve this research target, we have employed a dynamic recursive computable general equilibrium (CGE) model to analyze how the designed RR policies complement the ETS effects on emission abatement and economic growth. The results of the CGE model have confirmed the existence of a tradeoff between equality and efficiency. RR for income tax reduction is beneficial to emission abatement, but it has the worst performances on equality, Gross Domestic Product (GDP), and household welfare. RR for subsidizing renewable energy causes the lowest GDP loss, but it adversely impacts emission abatement owing to the induced economic boom. Lump-sum income transfer to low-income households is the best RR option because it is the most equitable way to use ETS revenues and induces the highest household welfare with satisfactory performances on emission abatement and GDP. Hence, ETS revenues are recommended to be transferred to low-income households.

This study analyses how public attitudes toward climate actions have changed over time in some of the biggest CO₂-emitter countries representing two categories of economies: the rich and developed vs. emerging. Using the World Value Survey data and two-sample tests of proportions, an exploratory analysis is conducted to understand the change in climate change attitudes in China, the United States, India, Russia, Japan, Germany, and South Korea over the last three decades. The study finds initial evidence of divergence in public opinion for climate actions across countries. The findings show that people in emerging economies (such as China and India) have cultivated more favorable views toward environmental protection and climate actions over time. They have started demanding better environmental policies and shown willingness to contribute to environmental protection both monetarily and symbolically. However, people in the developed and rich world are gradually moving towards less favorable climate opinions. Such startling changes in public attitude have the potential to impact future national and global treaties on climate change disparagingly.

To realize low-carbon transition and pursue sustainable development, China’s central government formulated the low-carbon city pilot (LCCP) policy. Current studies focus primarily on the effect of the policy at the macro level of the cities, including economic, industrial, technological, environmental dimensions. So far, few has looked at the effect of the LCCP policy on residents’ welfare. To address this gap, this study treats the LCCP policy as an exogenous policy shock and employs the Difference-in-Differences model to examine its influence on residents’ welfare from the perspective of ecological welfare performance (EWP). Additionally, this study integrates the concept of quality of life into the EWP evaluation system to provide a comprehensive reflection of residents’ welfare. The results demonstrate that the LCCP policy significantly increase EWP in pilot cities, with a series of robustness tests support this finding. Besides, mechanism examination indicates that the LCCP policy enhances EWP through optimizing industrial structure, promoting low-carbon technological innovation, and increasing digital economy. Moreover, heterogeneous results reveal a significant increase of EWP in eastern cities, non-resource-based cities, and cities with high government environmental governance intensity due to the LCCP policy. This study empirically confirms the positive role of the LCCP policy in improving residents’ welfare, provides evidence for synergistic development of other countries seeking to advance low-carbon transition alongside improvements in residents’ welfare.