Environmental Challenges最新文献

This study investigated corporate preferences for forest carbon credit offsets. The preferences were elicited through a choice experiment by administering a questionnaire to determine the price of credit, location of forest sequestration project offset, social development on local employment, preservation of ecosystem services, and unit of sustainable development goals (SDGs). We also examined stated preferences for credit offset and the willingness to pay. The analysis employed multinomial logit and random parameter logit (RPL) models to account for heterogeneity in preferences. The results showed that small and medium-sized enterprises (SMEs) held a neutral stance regarding offsetting their emissions and were categorized as being in the early stages of engagement with carbon offsetting. As SMEs can decide whether to purchase credit, we assessed their preferences for the type of credit-based forest carbon sequestration. SMEs had a significant preference for the location of the project, SDGs, and credit price when deciding to purchase credit based on the RPL model estimates. They were willing to pay JPY 6,191 (approximately USD 41) for the location of a project to be local rather than overseas, JPY 933 for a higher number of unit SDGs, and JPY 131 for an increase in the number of jobs. These results suggest that SMEs prefer purchasing local credit generated within their prefecture as a carbon offset alternative.

The process of urban growth often results in the conversion of agricultural spaces, including orchards. In Pakistan, Multan - widely known as the city of Mangoes - has seen exponential urban growth in the past couple of decades, resulting in a huge loss of Mango orchards to urban settlements. This research focuses on investigating local farmers’ motivations for selling Mango orchards to urban colonies and their perceived implications of transforming mango orchards into residential areas in Multan, Pakistan. By surveying 100 participants, the study captures insights into urban expansion trends, primary motivations behind selling agricultural land, and the social, economic and environmental consequences of such conversions. Descriptive statistics and correlation analysis (heatmap) are used to dissect the farmers perceptions on the drivers and implications of Mango orchards' conversion to housing settlements in Multan, Pakistan. Notably, 96% of respondents highlighted that orchards nearer to urban centers were predominantly targeted for conversion. Furthermore, 57% believed less productive orchards were more frequently turned into urban developments. Our correlation analysis provided clarity on the economic dimensions. Participants who felt their orchard was not a profitable venture tended to see greater economic advantages from selling their orchards. Interestingly, individuals motivated by a desire to 'improve quality of life' generally observed an improvement in their living conditions post-sale. On the environmental spectrum, concerns such as potential future temperature rises were consistently associated with several selling motivations, indicating a broad awareness of environmental consequences. This comprehensive research highlight the interplay of economic, social, and environmental factors in orchard-to-housing conversions, presenting valuable knowledge for urban development strategists and decision-makers.

Understanding the factors controlling spatial and temporal variability of atmospheric methane concentration (XCH₄) is crucial for mitigating its impacts and implementing emission reduction strategies. This study comprehensively investigates XCH4 and its driving factors (environmental, meteorological, and anthropogenic activity) across Iran over 20 years, from 2003 to 2022. It combines multi-source satellite observations, advanced spatiotemporal modeling techniques, correlation analysis, and machine learning algorithms. The spatiotemporal analysis showed notable spatial variation, with high XCH₄ levels in central, southern, and eastern Iran and lower levels in the northwest and north. Moreover, distinct seasonal cycles emerged, with maximum XCH₄ occurring during summer (August-September) and minimum levels in spring (April-May). Correlation analysis and variable importance assessment were developed to elucidate the key drivers governing XCH₄ dynamics. Correlation analysis revealed that vegetation cover, precipitation, and soil moisture were negatively correlated with XCH₄, while temperature indices showed a positive correlation, exhibiting the highest correlation in time dispersion and quantity among the studied variables. The Permutation Importance technique, used with a Random Forest classifier, a machine learning-based approach that considers the role of all variables together, showed that land surface temperature, wind speed, soil moisture, and vegetation cover are the dominant controls, with their importance ranked respectively. Surprisingly, anthropogenic emissions played a relatively minor role in shaping XCH₄ distributions at the regional scale. These findings highlight the significant influence of meteorological variables and ecosystem processes on XCH₄ modulation, revealing intricate Earth system feedbacks that inform targeted mitigation strategies and predictive models for curbing greenhouse gas emissions and mitigating climate change impacts.

Accurate prediction of depth and position of underground structures is a critical step in structural foundation surveys such as civil engineering excavations to adequately maintain the existing underground utilities. The study presents the results of comparative studies conducted to evaluate the performance of electrical resistivity tomography (ERT) alongside the VLF-EM method regarding depth estimation and location of buried targets of known materials, properties and dimensions. A laboratory test was carried out on the buried targets to determine the electrical properties of the buried targets before burial. The pre-burial geophysical investigation indicates no major anomaly within the site that could influence the geophysical response of the buried objects significantly. The results of the post-burial geophysical investigation indicate high variations in electrical resistivity values varying from 47 Ωm – 1081 Ωm (before) and 0.113 Ωm – 19,879 Ωm (after) the buried targets, while the VLF-EM data indicates that the current density values within the site were significantly influenced due to the presence of buried materials, confirming major influence or distortion of geophysical signature of the site. In post-burial ERT investigation, the Wenner and dipole-dipole (DD) arrays registered 67 % and 80 % degrees of alignment with the actual depth of the buried targets, respectively. Both Wenner and DD arrays show strength in depth estimation. However, the DD array indicates higher strength in terms of depth estimation and it is potentially suitable for near-surface utilities investigation due to its high precision in depth estimation. In comparison, VLF-EM captured six (6) out of eight (8) buried targets with a 47 % degree of alignment with the actual depth of the buried targets, which is far lower than the ER method, and may not be considered the most preferable method for geophysical prospecting where depth estimation of targets is of prime interest. However, the depth of targets varies from one method to another and one array to another.

South Asia has been experiencing recurring severe air pollution episodes in recent years. While many previous studies investigated such episodes focusing on individual cities and specific events, limited information exists on episode characteristics across multiple cities in this region. This study presents a comparative analysis of the characteristics of ambient PM_2.5 pollution episodes in 12 South Asian cities across five countries during 2019−2023. Daily mean PM_2.5 mass concentrations were decomposed into trend, seasonal, and residual components, and episodes were identified through anomalies in residuals. Furthermore, pollution episodes were characterized using magnitude, frequency, duration, and a relative severity index. The cities exhibited annual mean PM_2.5 mass concentrations ranging from 20.6 ± 2.5 μg m⁻³ (Colombo) to 116.6 ± 9.3 μg m⁻³ (Lahore), with six out of 12 cities having annual mean PM_2.5 mass concentrations > 50 μg m⁻³. Additionally, significant increasing trends (p < 0.05) in PM_2.5 levels were observed for Dhaka, Chennai, Hyderabad, Kolkata, Islamabad, and Lahore (Sen's slope: 1.00−4.33 μg m⁻³ y⁻¹), whereas decreasing trends (p < 0.05) were observed for Mumbai (−0.74 μg m⁻³ y⁻¹) and New Delhi (−2.00 μg m⁻³ y⁻¹). Mean PM_2.5 episode magnitudes varied in a wide range from 49.9 ± 6.1 μg m⁻³ (Colombo) to 367.1 ± 17.9 μg m⁻³ (Lahore) across the cities. Likewise, the mean episode frequency ranged from 1.6 y⁻¹ (Kathmandu) to 5.2 y⁻¹ (Dhaka), whereas duration ranged from 1.2 (Mumbai) to 2.6 (Kathmandu) days per episode. Based on the relative index of episode severity, Lahore, Dhaka, and New Delhi exhibited high episode severity, as well as high baseline PM_2.5 levels. In contrast, Karachi, Islamabad, Hyderabad, and Kathmandu showed moderate episode severity and moderate baseline PM_2.5 levels, whereas Colombo and Mumbai showed low episode severity with low to moderate baseline PM_2.5 levels. Moreover, annual PM_2.5 episode severity ranks among the cities changed dramatically during 2019−2023. The relative severity of baseline and episodic pollution levels presented in this study may help policymakers prioritize the control strategies targeting pollution episodes, long-term trends, or both, as well as protecting human health through mitigation, preparedness, and forecasting. The findings will also provide insights for formulating regional policies aimed at transboundary cooperation and collaboration to deal with air pollution challenges across South Asia.

Assessing soil organic carbon (SOC) is vital for water retention, soil health, nutrient cycling, greenhouse gas emissions, and pollutant reduction and thereby contributes to sustainable agricultural production and food security. Thus, using long-term climate, soil, and land management inputs, the Rothamsted Carbon (RothC) model was applied to assess the current and future SOC stocks in the Anjeni watershed using long term climate, soil and land management data. RothC was calibrated with long-term SOC, land management, and climatic data from the Anjeni watershed in north-west Ethiopia. The correlation coefficient between simulated and observed SOC in 1997 and 2021 were 0.77 and 0.86, respectively, suggesting that the model could characterize the SOC of the Anjeni watershed. Then, the RothC was used to estimate SOC in the watershed for 30 years, from 2022 to 2052, under three slope gradients and four land use type and carbon storage scenarios (business as usual (BAU), low, medium and high carbon inputs). The result indicated that in the lower slope gradient, the current SOC simulation is less than all future scenarios considered under all land use types. Grass/fallow land showed higher current and projected SOC than cultivated land and plantation forest. Moreover, grass/fallow land with a gentle slope gradient had higher SOC than the watershed's middle and high-elevation parts. Overall, the model projected an increase of SOC under different future scenarios that could be due to climate and land use cover changes, the long-term soil-water conservation camping works and better soil and land managements in the watershed. This future assists for water retention, soil health, nutrient cycling, soil aeration, and greenhouse gas emission reduction, which in turn could enhance agricultural productivity, food security, and sustainable development.

Producing rice generates straw, which is often conveniently burned, causing substantial atmospheric pollution. Increasing straw utilization efficiency and reducing straw burning will improve the agricultural ecological environment and promote sustainable development of agricultural production. Little is known about farmers’ perceived costs of alternative straw management practices and what it would take for them to stop straw burning. We conduct choice experiments with 543 Vietnamese farmers in the Mekong Delta to elicit their preferences for improved straw management practices under varying monetary incentives and enabling environments of mechanization and governance. The attributes and levels used in this study include sustainable practice (i.e., incorporation of rice straw, partial removal, and complete removal), availability of machinery (i.e., low, medium, and high), governance (i.e., individual farmer, farmer organization, and local government), and monetary incentives (US$43–87/ha). Results from a mixed logit model suggest that farmers are willing to stop straw burning and adopt sustainable straw management practices in return for monetary incentives. Farmers require lower monetary incentives when machinery services for chopping and collecting rice straw are available and when rice straw management is governed collectively by farmer organizations or the local government. Policy makers can use these results to prioritize investments and design optimal policies for mitigating air pollution by diverting farmers away from straw burning towards sustainable rice straw management practices.

With the increased emphasis on charting ocean sustainability narratives, marine spatial planning (MSP) is envisioned as a key tenet. MSPs emphasize the systematic and collaborative planning and management of ocean space (resources and activities) for the benefit of all users. Regions that have implemented MSP based on collaborative stakeholder engagement are progressively realizing better ocean sustainability outcomes. Unfortunately, in developing coastal states, progress toward MSP is largely pedestrian and has attracted less interest. This is partly due to archaic coastal/marine resource models that are dominated by few powerful stakeholders. This is worsened by increasing human-environmental shocks, which are creating bleak futures. Our study systematically sourced 12,316 documents from Scopus that were analyzed using bibliometrics to (i) conduct a performance analysis, (ii) conduct a scientific mapping analysis and (iii) identify game-changing developments that can drive ocean sustainability. A performance analysis revealed that even though scholarship and publications on MSP have increased globally, scholarship among or led by researchers from the global south are limited. Scientific mapping analysis revealed emerging positive trends in multi-country collaborations as well as the recognition of threats to the marine environment. Reversing this requires increased stakeholder engagement. However, how to achieve this goal in most developing coastal states has been less studied. Building on this, we developed a novel Collaborative Stakeholder Engagement Pathway (CoSEP) involving eight (8) interrelated steps that can help build collaborative engagements for MSP development and ocean sustainability. A notable takeaway from the CoSEP is that; since research on MSP development is limited or in its infancy, knowledge of how and when to engage which stakeholders is key in creating collaborative mechanisms for positive ocean sustainability, including ocean justice. This can help localize sustainable ocean development pillars and build avenues for integrated coastal resource management. Using participatory approaches that bring forward microlevel stakeholder perspectives could be a future driver in designing effective interventions and cultures to create MSPs that meet ocean sustainability targets.

Street, wall, and rooftop greening systems are essential for urban heat reduction and carbon neutrality. In this study, we compared the temperature-reducing effect of current and developed technologies that maximize the latent heat of evaporation through such greening systems. A research site with the maximum urban heat island effect was selected by analyzing the vulnerability of Suwon City, Korea. The latent heat of evaporation for each method was determined by conducting actual measurements and verified by performing computational fluid dynamics simulations. Based on the results of statistical techniques, the validated model was highly reliable. When developed technologies were applied, the temperature of the entire city was reduced by approximately 2 °C. Compared with the existing street greening system, the developed technology achieved a temperature reduction effect even at a distance of 5 m. Current wall greening systems only have a temperature reduction effect at 1 m, but that of the developed technology was approximately 1 °C even at a distance of 2 m. The existing rooftop greening system had a temperature reduction effect only at the height of 1.2 m, whereas that of the developed technology was effective even at 6 m, contributing to a reduction in the temperature of the entire city.

Climate change is a global problem that dramatically affects natural resources, resulting in significant changes in temperature, precipitation, and humidity, which affect vegetation cover. Under this light, this study aimed to identify the potential of remote-sensing techniques to reveal the spatiotemporal response of vegetation cover to climate change in the western Makkah Province using Landsat-5 Thematic Mapper, Landsat-8 operational land imager, Global Land Data Assimilation System model, Global Precipitation Measurement, and Famine Early Warning Systems Network Land Data Assimilation System model data from 2000 to 2023. Optimised Soil-Adjusted Vegetation Index (OSAVI), classification, overlay, change detection, and correlation analysis were utilized to process data. Time series analysis of data revealed climate-related changes which were particularly intense in recent years. Specifically, temperature, precipitation, and specific humidity were found to differ depending on the landforms and season. Temperature was higher during the dry season compared to the wet season. A decrease was observed in the overall precipitation rate, which did not exceed 81.39 mm during the wet season and approximately 11.46 mm during the dry season. Additionally, precipitation increased in 2023 but decreased in 2018. Moreover, the study area was located on semi-arid lands for all years except for the wet season of 2023. OSAVI analysis, which is sensitive to climate change, revealed that vegetation coverage can be both positively and negatively affected by climate change. The most profound vegetation coverage in the study region was observed in 2023. A strong correlation was also observed between precipitation and vegetation in the study area, which showed less high-greenness in the dry season and more widespread grasses. The implications of these findings for the development of strategies for biodiversity conservation in semi-arid regions are significant.