Environmental Challenges最新文献

Artificial intelligence (AI) is an umbrella term for a wide range of machine intelligence systems that can replicate the behavior of humans. AI and Big Data have emerged as defining characteristics of the fourth industrial revolution (IR). AI has developed tools. Because of the novelty, the investigation of IR, AI, and their environmental effects is still in the early stages of exploration. This study investigates how IR and AI affect human and environmental health and also discusses IR, AI, machine-human ideas, innovation, and AI's environmental benefits, further examines the challenges of these innovations, and recommends additional studies to explain their progress. As a result, the application of AI technology in environmental management, particularly concerning pollution, has become a significant advancement in reshaping our approach to monitoring the environment. Numerous countries are reaping substantial advantages by integrating AI in creating, executing, and assessing measures to address environmental degradation. These innovations can yield societal advantages and contribute to achieving the Sustainable Development Goals (SDGs) 2030; unfortunately, it is important to acknowledge that these benefits may not align well with environmental sustainability objectives, and the increasing number of electronic gadgets presents an additional concern. Conducting future research is crucial to investigate the growing prevalence of electronic devices utilized for AI, its potential ramifications for the future trajectory of climate change, and the approaches being taken to address the issue. Future research should prioritize conducting lifecycle environmental impact analyses, developing sustainable AI hardware, optimizing renewable energy usage, advancing climate modeling techniques, finding effective solutions for managing e-waste, utilizing AI for environmental monitoring and protection, conducting socio-environmental impact studies, developing policies and regulations, creating energy-efficient AI algorithms, and integrating circular economy principles to ensure that AI advancements align with environmental sustainability.

Extended producer responsibility (EPR) as a policy principle has been in practice in different product-country settings for a few decades now, and yet its implementation, as well as the outcomes, have varied. Amid the sustainability crises posed by the take-make-dispose-based linear economy and the transgression of the various planetary boundaries, governments worldwide are increasingly embracing CE-related policies, more so in the EU, with the Green Deal ushering in a wave of legislative transformations. Given this background, there is an urgent need for both policymakers and manufacturers to transition EPR from a mere waste management strategy to what can be termed as an “interface policy”, covering the interdisciplinary areas of circular economy, chemicals and waste law.

Drawing upon insights from academic and grey literature, this paper contributes to characterising the system architecture underpinning EPR. It develops a conceptual framework using a life cycle thinking process through which EPR can be analysed, agreed upon, and operationalised. Such a conceptual framework can be helpful in governmental bodies tasked with framing effective EPR policies and for industry stakeholders responsible for take-back system design and implementation.

Tanzania is emerging as a key coffee producer, but climate change poses a serious threat to coffee production in the country. While extensive literature addresses climatic variables’ trends and variability in coffee-growing regions globally, elevation, a crucial factor in coffee cultivation, is often overlooked. This paper aims to fill this knowledge gap by examining the future trends and variability of climatic variables identified in previous studies being crucial for Arabica coffee (rainfall, temperature, and vapor pressure Deficit (VPD)) during different phenological stages of Arabica coffee growth across three elevation zones (lowland, midland, and highland). We study the Mbinga district, now a major Arabica-producing area and the forefront of hopes for expanding production in Tanzania. We used a Multi-Model Ensemble (MME) of two Global Circulation Models (GCMs) to estimate the future trends and variability of rainfall, temperature, and VPD under SSP2-4.5 and SSP5-8.5 scenarios from 2023 to 2064. We analyzed these data using non-parametric trend analysis (Mann-Kendal and Sen's slope test estimation) and standardized anomalies. Our results suggest that both temperatures (minimum and maximum) and VPD during critical flowering and growing seasons are projected to increase significantly over the next 42 years across all elevation zones under both emission scenarios. The mean annual temperatures across elevation zones in Mbinga are projected to surpass the optimal temperature envelope (23 °C) for producing quality Arabica, challenging the notion that the highlands might serve as a refuge for future production. The mean annual VPD in the lowlands are projected to surpass the optimal envelope (0.82 kPa) for Arabica productivity from 2023 to 2064 under both emission scenarios, while midlands are projected to surpass the optimal VPD envelope from mid-2040s onwards in constrast, the highlands are projected to stay within the optimal VPD envelope for Arabica productivity from 2023 to 2064 under both emission scenarios. Nevertheless, as highlands are projected to get more warmer in the future because of rising temperatures, this hope might not be feasible in the post-2064. To secure Mbinga's future in global coffee value chains, the Tanzanian government and stakeholders must support adaptation measures, enhancing smallholders’ resilience against climate-induced disruptions.

Amidst the ongoing global climate change crisis, characterized by unpredictable weather patterns and increasing environmental shocks, farming communities are among the most vulnerable. This vulnerability is particularly evident in Tigray, Ethiopia. This study, which investigates the incidence of climate change and its variability in relation to risk perception and food security of farming communities in Tigray, has significant implications for future action. It uses climate and the Living Standards Measurement Study panel data. A multi-method analytical procedure is employed to assess changes in temperature and rainfall, understand farmers’ climate change risk perception, and signify the welfare effects of droughts on farmers. The study finds significant warming trends, particularly increasing warming nights, and variable but increasing rainfall trends. Farmers do perceive climate change as a reality (85.53%) and a threat to their welfare (23%). Cooperation, diversification, irrigation, wealth, drought, and access to services were identified as drivers of this risk perception. Similarly, the study found drought is a significant driver of food insecurity. Other variables associated with food security status substantially are wealth, head marital status, and non-farm participation. It concludes that climate change is a reality and a perceived risk in Tigray. It poses a threat to the lives and livelihoods of its farming communities. It is recommended that farm households’ adaptive capacity be enhanced through improved livelihood options, promotion of cooperation, development of irrigation schemes, and increased access to weather and climate information.

With the various impacts of global warming and climate change on businesses, corporate climate change disclosure has become a highly prominent topic in recent years, drawing significant attention from various sectors of society. In emerging economies experiencing rapid economic growth, such as China, climate change disclosure holds significant relevance for businesses in presenting their sustainability efforts to their stakeholders and society. Therefore, it is necessary to investigate the key factors propelling climate change disclosure. This study focuses on listed companies in the pilot regions of China's Emissions Trading Scheme (CN-ETS) to explore the influence of external factors on disclosure performance. The difference-in-differences approach is conducted to examine the impact of ETS policy implementation on corporate climate change disclosure. The results indicate that ETS does have a certain level of effect on disclosure performance. Additionally, the influence is more significant for companies facing higher government pressure. Our findings provide several policy implications for the future development of climate management and corporate climate change disclosure practices in China and other emerging economies.

Central Java Province is one of the rice production centers in Java Island which plays a role in supporting national food availability. The phenomenon of a decrease in the number of agricultural business households over 10 years by 16.3 % indicates a shift in the interest of people working in the agricultural sector, switching to other sectors outside the agricultural sector. The lack of interest of the younger generation in farming indicates the phenomenon of a shift in community interest in the agricultural sector. This is inseparable from the regeneration of farmers in rural areas. This research uses the basic method of descriptive analysis. The research location was determined by purposive method so that the district with the largest average harvest area and rice production, namely Grobogan District and the location that reflects the adoption of technology, namely Sukoharjo District. The number of research samples was 250 samples of rice farmers in Central Java Province. Factors affecting the regeneration of rice farmers in Central Java Province were analyzed by SEM PLS. The results of the inner model analysis showed (1) latent variables of individual characteristics, socio-cultural environment, and farmer empowerment significantly positive effect on the performance of rice farmers, (2) economic environment variables and farm management had, a significantly negative effect on the performance of rice farmers, (3) latent variables of socio-cultural environment, economic environment, and performance of rice farmers have a significant positive effect on the regeneration of rice farmers in Central Java Province and (4) variable farmer empowerment has a significant negative effect on the regeneration of rice farmers in Central Java Province. The results of the model fit analysis and quality indices show that the model fit test has been fit (ideal).

Faced with increasing water scarcity, the potential of greywater reuse stands out, but requires effective treatment of to remove organic matter, pathogens, surfactants, and suspended solids. Constructed Wetlands (CW) are sustainable and decentralized technologies gaining increasing prominence for this propose. They are recognized for their low-cost, simplicity, and effectiveness in wastewater treatment, producing effluents that meet quality standards for reuse. Despite their advantages, there is a need for a deeper understanding of the factors that influence their performance and efficiency. This review aims to fill this gap by systematically analyzing the current research on CW technology, identifying key variables that affect its application and potential for improvement. A systematic review considering the period 2019–2023 was carried out using Methodi Ordinatio, a multicriteria decision-making methodology. The search databases were Science direct, Web of science and Scopus. This approach involves structures process for selecting scientific articles, resulting in a bibliographic portfolio of recent studies,. The initial search yielded 291 retrieved titles, and through multicriteria selection, 48 studies were chosen for in-depth analysis. Existing studies allowed to evaluate the ways in which CW systems are applied. The review highlights how CW systems are applied, the influence of substrates type, plants, and operational criteria which emerged as the primary factors influencing the technology's performance. This review also highlights the growing use of construction waste and biochar as substrates, which have shown promise in enhancing CW efficiency. Despite the focus on greywater "reuse", for articles selection, it was observed that the topic was scarcely addressed, thus suggesting that studies on CW application for greywater water reuse remains underexplored. This review provides a state-of-the-art synthesis of CW technology, offering valuable insights into how specific design and operational choices impact system effectiveness. It serves as a worthwhile resource for enhancing the efficiency and application of CW in sustainable wastewater management.

Air pollution is gradually becoming a major health concern globally and Nigeria is especially susceptible to air pollution Available data indicates that vehicular traffics, industries, wildfire and biomass burning are significant sources of air pollutants such as Particulate Matter PM, Carbon Monoxide CO, Nitrogen Dioxide NO2, Sulphur Dioxide SO2 and Volatile Organic Compounds VOCs in the air in Lagos State. The aim of this study is to perform geospatial modelling of air pollution around Olusosun and Solous dumpsites in Lagos State. The study analyses, assesses, and predicts air pollution emission in these dumpsites and examines their possible health impact on residents. Using both ground and satellite-based data, different GIS techniques were used to model this pollution concentration. These methods include Kriging, Regression, and Landfill Gas Emissions Model (LANDGEM) which helps to analyse, model and predict the air pollutant levels around these dumpsite. The average pollution level of Particulate Matter with a diameter that is 2.5 micrometers (PM2.5) in Olusosun was 94.29 μg/m3 while those in Solous was 94.12 μg/m3 over a 24 h period. This is way above the Air Quality Guideline recommended by the WHO which is 15 μg/m3 over a 24 h period. This is similar to the high pollution level of NO2 which was 237.64 μg/m3 in Olusosun and 255.84 μg/m3 in Solous as against the WHO Air Quality Guideline of 25 μg/m3 over a 24 h period. SO2 level is also varyingly high in Olusosun and Solous. The reading of 109.27 μg/m3 in Olusosun is considerably high as compared to the reading of 43.82 μg/m3 in Solous. However, both readings are relatively higher than the Air Quality Guideline recommended by the WHO which was 40 μg/m3. The CO air quality level of 0.55 mg/m3 in Olusosun and 0.52 mg/m3 in Solous is within the accepted level of the Air Quality Guideline recommended by the WHO. This shows that CO is not a pollution threat in the major dumpsites in Lagos. This is similar to the readings of PM5 and PM10 which are within the WHO Air Quality Guidelines over a 24 h period. The Moderate Resolution Imaging Spectroradiometer (MODIS) data shows a trend of increasing concentration of PM2.5 over the 5 study months due to an increase in the quantity of waste disposal, which shows that high waste disposal is directly proportional to high level of PM2.5. It is recommended that living farther (more than 15 km) away from the dumpsites can reduce the high risk of air pollution and its related diseases for residents. Also, there is need for proper incineration and maintenance of waste to reduce the level of air and other pollutants in and around the dumpsites.

To address the increasing global levels of CO₂ emissions, it is necessary to develop efficient strategies that can convert CO₂ into useful chemicals such as carbonates, ureas, and carbamates. Hybrid systems that combine direct capture and subsequent utilization of CO₂ offer a unique emission-controlling pathway for achieving a carbon-neutral society. Ti-Zr mixed oxides could be ideal candidates for CO₂ adsorption owing to the high specific surface areas pore sizes. Therefore, in this study, metal oxides with desirable acid-base sites that is, Ti_xZr_(1−x)O₂ with different Ti/Zr ratios were synthesized using combined sol-gel and solvothermal methods and then utilized to adsorb and synthesize ethylene urea (EU), an important precursor of pharmaceuticals and agricultural products. The results indicated that the CO₂ adsorption capacity of TiO₂-ZrO₂ mixed oxides were higher than that of TiO₂ alone at 100 kPa and 25 °C, corresponding to their higher specific surface areas and pore volumes compared to their individual oxide counterparts. CO₂-adsorbed Ti_xZr_(1−x)O₂ was subsequently used as a CO₂ source to generate EU through a reaction with ethylenediamine (EDA) and 2-propanol. Notably, samples containing higher proportions of Zr-Ti_0.3Zr_0.7O₂ and ZrO₂ produced EU in significant amounts, owing to their acid-base bifunctionality. Overall, EU was synthesized using CO₂-adsorbed Ti_xZr_(1−x)O₂ as a CO₂ source and as an accelerator for reacting with EDA, without requiring high-pressure or high-purity CO₂.

Globally, landslides in mountainous regions are considered to be major geo-hazards which are frequent, intensive and devastating that result in extensive destruction to people's lives, land, property and other infrastructure. To mitigate landslide-associated risks, identification, evaluation, and delineation of susceptible areas is essential for proper strategic planning and management. The present study was conducted in Blue Nile Gorge in the highlands of Central Ethiopia, about 185 km from Addis-Ababa, along the Gohatsion-Dejen road corridor. Landslides are frequently reported in the study area, particularly during the rainy season. The objective of the present study was to evaluate landslide susceptibility and to prepare a landslide susceptibility zoning (LSZ) map. For this purpose a statistical information value (IV) model was used that took into account various factors including slope material, elevation, slope, aspect, land-use and land-cover, proximity to the road, and proximity to streams. Each of these factors was classified based on different conditions like landslide concentration, topography, geology, and land-use and land-cover types. The landslide inventory mapping was conducted through field observations and Google Earth image interpretation. Later, the influence of each causative factor on past landslides was used to calculate their respective IVs. The statistical IVs were further used to evaluate the landslide susceptibility. The LSZ map produced from this study shows that 22.3 % of the study area falls into a very high susceptibility zone, 23.1 % into a high, 18.8 % in moderate, 23.1 % into a low, and the remaining 12.8 % into a low susceptibility zone. The LSZ map was validated using the Receiver Operating Characteristics Curve (ROC), landslide density index (LDI), and past landslide data. All validation methods showed acceptable results. The prepared LSZ map can be used for safe land-use planning of the area.