Environment, Development and Sustainability最新文献

Forest fire poses a major environmental hazard, to the extent of sometimes permanently damaging the forest ecology. The potential of human to repair nature is hampered by the extension of human domination into forests, which results in the loss of forest land. While human expansion cannot be stopped, we must accept responsibility for the consequences and thus work to minimize such environmental hazards emanating from such calamities. RS and GIS have proved to be useful techniques for such studies. The goal of the current study is to identify the most vulnerable forest fire zones in the West Sikkim district falling within the state of Sikkim (India) during 2004–2021. Various thematic layers (LULC and topographical factors) were created using Landsat 8 OLI and ASTER DEM. For the final forest fire susceptibility zone (FFSZ) map, climate variables such as precipitation, temperature, humidity, and wind speed were also used. The authors employed the MCDM techniques of AHP and TOPSIS to determine the areas which are most vulnerable to wildfires in the research area. 194 wildfire locations, as obtained from Sikkim State Disaster Management Authority (SDMA) were used for the classification. The FFSZ were classified as “very high, high, medium, low, and very low vulnerability zones” based on their fire vulnerability. The areas under the “Very High Susceptibility Zone” of AHP and TOPSIS were 152.331 km² and 348.499 km² respectively whereas the areas under “Very Low Susceptibility Zone” were 115.351 km² and 139.436 km² in the results of AHP and TOPSIS respectively. To check the accuracy of the FFSZ susceptibility maps obtained from the two modelling techniques, the same was confirmed by using (Receiver Operating Characteristics) ROC curves. The result indicates that the TOPSIS model (AUC = 82.28%) is slightly better at determining the vulnerable zones than the AHP method (AUC = 72.25%).

This research aims to determine whether institutional quality thresholds exist in the relationship between economic growth and carbon emissions for 99 countries from 2008 to 2015. Using a panel threshold autoregressive estimation, the findings reveal a significant threshold effect of institutional quality in the carbon emissions -economic growth nexus. Carbon emissions rise in response to economic growth when institutional quality is below the threshold; on the other hand, emissions decrease in response to institutional quality above the threshold. The results confirmed the existence of the environmental kuznets curve. Further, the results also validated through robustness testing by splitting the sample into developed and developing countries, indicating that economic growth with better institutional quality can help reduce emissions. Therefore, as a fundamental tactic to address environmental issues alongside economic growth and establish the path for sustainable development, policymakers should concentrate on strengthening institutional quality.

Green development has become an essential approach to overcoming environmental constraints along with the new urbanization process in China. The objective of this study is to explore the interrelationship and coordination between new urbanization and green development across 30 provinces in China from 2005 to 2020 utilizing the global entropy, coupling coordination degree, and panel vector autoregression models. Results revealed that China has experienced high speed growth rates of new urbanization and green development with 1.568 and 0.716 times, respectively. The coupling coordination degree between new urbanization and green development demonstrates an overall increasing trend with large heterogeneity among each province; its spatial pattern shows a decreasing trend from east (0.744) to west region (0.639) in 2020. From the viewpoints of interaction between the two indicators, new urbanization hinders green development in the early periods (except for the eastern region), and its influence disappears as the number of periods increases. Overall, a positive interaction between new urbanization and green development exits, but the coupling coordination degree exhibits a strong imbalance across Chinese provinces. Especially, 36.67% of provinces which is concentrated in the western and northeastern regions remain insufficiency and imbalance in the coupling coordination degree. Policymakers should ensure coupled coordination between new urbanization and green development and address interregional disparities. The results of this study can facilitate Chinese provinces or other developing countries in formulating policies and actions to achieve sustainable development through coordinated efforts in new urbanization construction and green development.

The agriculture industry is facing a difficult situation as an outcome of mounting food demand, food security, climatical situations, legislative laws, etc. Moreover, the practice of chemical fertilizer in agriculture adds to greenhouse gas emissions and large water consumption. As a result, agricultural systems must be rethought to become more robust and sustainable. Regenerative agriculture, a collection of agricultural methods and techniques that prioritize long-term organic farming, environmental responsibility, and sustainability, is a potential solution to these issues. This review intends to discuss the interplay between regenerative agriculture (RA) and digital agri-technologies to attain sustainability. The significance of RA over conventional agri-technologies, the role of nanobiotechnology, smart sensors, and other fourth Agri-tech 4.0 innovations to augment food production are emphasized. Growing evidence shows that digital technologies (particularly the Internet of Things (IOTs), big data, artificial intelligence, and blockchain) would have a greater impact on RA. These practices can have an array of environmental benefits. Precision agri-technologies like internet-of-things (IoTs), cloud computing, blockchain, satellites, drones, use of nanosensors, and remote sensing of fresh produce during the supply chain can augment food production at a commendable rate. Implementation of automated harvesting processes to attain minimum contact harvesting and post-harvest operations are quite useful in increasing food production. Thus, current strategies toward agricultural transformation and novel government policies, toward sustainable regenerative agriculture could be a game-changer in sustainable agriculture. These implications are derived through carefully analysing the published literature we have amassed using various search engines. However, more research is still needed to entirely harness the benefit of progressive technologies, to realize a healthier and further sustainable agriculture future.

A significant challenge in offshore wind energy is understanding and reducing wake losses from wind farms, which can affect downstream turbine efficiency by 10–20% and determine the optimal capacity of wind farms within a defined area. This study aimed to determine the optimal wind farm capacity density in offshore subzone B₁, which is off the coast of Tamil Nadu, India. The objectives include maximizing the installable offshore wind capacity, achieving the highest possible annual energy production or capacity utilization factor (CUF), maintaining array losses below 10%, and minimizing the levelized cost of energy (LCoE). The methodology involves analysing the ERA5 reanalysis of wind data, assessing various wind farm capacity densities (3–7 MW/km²), and evaluating the impact on turbine spacing, array losses, and LCoE. This study revealed a significant correlation between the wind farm capacity density and the LCoE, indicating an upwards trend in the LCoE with increasing capacity density. An optimal density of 5.17 MW/km² was identified for subzone B₁, accommodating 72 turbines with a total capacity of 1080 MW and an LCoE of Rs. 8.86/kWh. This configuration balances energy production and costs while providing critical information for future offshore wind projects in the region. This study underscores the importance of strategic turbine placement and continuous innovation in wind energy research.

This study aims to assess the long-term impacts of climate change on rainfall erosivity in the Jhelum Catchment, India. The primary research question addresses the temporal variation in erosivity under different Shared Socioeconomic Pathways (SSP) scenarios, employing General Circulation Models (GCM) from the CMIP6 phase. Six GCMs, including ACCESS-CM2, CanESM5, INM-CM5-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, and MPI-ESM1-2-LR, were utilized to analyze rainfall erosivity. The study explores the correlation between erosivity and climate change by incorporating SSP scenarios (SSP245 and SSP585) over the period 2020 to 2090. The methodology involves a detailed examination of model correlations and statistical precision. The study reveals a progressive rise in Rainfall Erosivity (R) values, indicating heightened susceptibility to soil erosion from 2020 to 2090. Notably, IPSL-CM6A-LR and MPI-ESM1-2-HR models exhibit positive correlations with IMD precipitation, establishing their suitability for analyzing climate change effects in the Jhelum Catchment. The average R value increases from 798.804 (MJ-mm/ha/h/yr) in 2020 to projected values of 1551.57 by 2090 under SSP585, highlighting the substantial impact of climate change on erosivity. The results underscores the urgency of addressing climate-induced soil erosion in the Jhelum Catchment. The implications extend beyond local contexts, providing valuable insights for global climate change resilience. By enhancing our understanding of erosivity dynamics, this research contributes to interdisciplinary efforts and calls for proactive measures in sustainable land management and environmental policy.

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The utilization of renewable energy is closely linked to the attainment of sustainable development goals (SDGs). In the context of climate change, examining how climate change affects renewable energy consumption is crucial. In theory, climate change is expected to prompt governments to implement policies conducive to developing renewable energy, thereby increasing renewable energy consumption. Regarding the empirical research, we construct and measure the climate change index, policy response index, and renewable energy consumption index of 30 provinces (cities) in China from 2000 to 2021 and then test the relationships between the three variables using fixed effects and mediating effects models. The results show that (1) climate change significantly promotes renewable energy consumption, with stronger promotion observed in economically developed provinces (cities) (r_High=0.848 > r_Low=0.235) and heavily polluting enterprise-intensive provinces (cities) (r_Intensive=3.712 > r_Others=0.776); (2) the policy response has been proven to be a partial mediating effect of climate change on renewable energy consumption, with statistical significance at the 1% level; (3) in contrast to prior studies, we reveal that foreign direct investment (r=-30.449) and trade openness (r=-0.787) exert negative influences on China’s renewable energy consumption, whereas the total dependency ratio (r = 4.815) positively impacts China’s renewable energy consumption. China needs to address the regional disparities in the policy response to renewable energy development and should promote industrial restructuring, strengthen environmental supervision, and guide investments in renewable sectors.

The escalating global demand for energy to ensure thermal comfort in buildings is a significant concern primarily due to widespread air conditioning use in residential and commercial sectors. Roofs, covering 20–25% of urban surfaces, play a crucial role in heat gain during summers. Implementing insulated roofs is a sustainable solution to maintain optimal building thermal conditions. Previous research shows that insulated roofs significantly reduce energy consumption, effectively mitigating the Urban Heat Island effect. This paper presents the outcomes of a comparative analysis conducted through eQuest building energy modeling software, to identify the most suitable insulation materials for diverse climatic conditions in eight Indian cities, each characterized by its unique climate profile. The study integrates findings related to the thermal effectiveness of insulated roofs employing different insulation materials across these varied urban environments. This comparative investigation examines parameters such as payback period, greenhouse gas emissions, reduction in energy consumption, and optimal insulation thickness to determine the most appropriate insulation material for specific climatic conditions. Insulated roofs using various insulation materials in selected Indian cities have demonstrated significant energy savings, ranging from 33 to 89%. Moreover, there has been a notable reduction in greenhouse gas emissions, from 60 to 90%. The optimal thickness for insulation typically falls between 0.0115 and 0.0560 m. PUF, Peripor, and Neopor have emerged as standout performers among the materials tested. The study highlights how insulated roofs reduce energy demand, enhancing sustainability and cost-effectiveness for engineers, researchers, architects, and residents.

An environmental impact assessment of the Yangon waste-to-energy plant was performed using a modified version of the Leopold matrix to analyse the environmental and socio-economic implications. The results revealed significant environmental impacts on air, water and soil as a result of the inadequate management of incineration ash and leachate. On the social front, the project did not present any major concerns. The assessment also showed that gate fee and revenue from electricity production were not sufficient to meet the operating costs. Overall, the environmental impact assessment revealed that the project is not sustainable. Mitigation measures for significant impacts, particularly the implementation of adequate pollution control technologies for air pollutants and leachate, were suggested, along with monitoring plans. Additionally, supporting policies and the specification of emission standards for potential contaminants were recommended to enhance sustainability.

Wheat is one of the most important cereal crops cultivated in a wide range of agro-ecologies in Ethiopia. It is also the source of food for the majority of Ethiopian people, next to maize. However, factors such as climate change and other challenges have contributed to its consistently low productivity. Therefore, this study aimed to analyze the impact of climate-smart agriculture practices (CSAPs) (wheat row planting, crop rotation and improved wheat variety in isolation and in combination) on the technical efficiency of wheat farmers. The data were generated from 385 randomly selected wheat producers, encompassing 702 plots across three prominent wheat-producing districts in northwestern Ethiopia. A stochastic production frontier (SPF) with selection correction model and a multinomial endogenous switching regression (MNESR) model were applied to estimate the technical efficiency and the impact of CSAPs on technical efficiency, respectively. The estimated mean technical efficiency of wheat farmers was 84.5%, ranging from a minimum of 32.8% to a maximum of 99.8%. The MNESR model result showed that the adoption of CSAPs in isolation or in combination considerably improved wheat farmers’ technical efficiency. The highest technical efficiency was recorded when farmers implemented all three CSAPs simultaneously within a single plot, rather than when they adopted them separately. This implies that policymakers and stakeholders should promote the adoption of a combination of CSAPs to enhance wheat productivity.