Resources Environment and Sustainability最新文献

Banana cultivation plays a pivotal role in Tanzania’s agricultural landscape and food security. Precisely forecasting banana crop yield is essential for resource optimization, market stability, and informed policymaking, particularly in the face of climate change. This study employed time series and ensemble models to forecast banana crop yield in Tanzania, offering crucial insights into future production trends. We utilized Seasonal ARIMA with Exogenous Variables (SARIMAX), State Space (SS), and Long Short-Term Memory (LSTM) models, chosen based on regression analysis and data exploration. Leveraging historical banana yield data (1961–2020) and relevant climate variables, we formulated an ensemble model using a weighted average approach. Our findings underscore the potential of time series and ensemble models for accurate banana crop yield forecasting. Statistical evaluation metrics validate their effectiveness in capturing temporal variations and delivering reliable predictions. This research advances agricultural forecasting by demonstrating the successful application of these models in Tanzania. It emphasizes the importance of considering temporal dynamics and relevant factors for precise predictions. Policymakers, farmers, and stakeholders can leverage this study’s outcomes to make informed decisions on resource allocation, market planning, and agricultural policies. Ultimately, our research bolsters sustainable banana production and enhances food security in Tanzania.

Accurate and rapid censuses can provide detailed basic information for a country, which is useful for resource allocation, disease control, disaster prevention, urban planning, and business management. However, traditional censuses often take up much time, manpower, and financial resources. Population maps are created by national statistical institutes at statistical units. Remote sensing imagery combined with end-to-end deep learning models makes it possible to estimate a wide range of populations at a low cost. This study demonstrates the effectiveness of a local–global dual attention network (LGANet) for population estimation using remote sensing images. The LGANet contains a local attention embranchment and a global attention embranchment on the top of the backbone to adaptively learn and integrate two discriminative features simultaneously. To enhance the precision of population estimation, the outputs from the two attention modules are combined. This method utilizes daytime remote sensing images as input, complemented by nighttime light data, to estimate the population on 1 km grids. Our method exhibits superior accuracy compared to other deep learning methods, as evidenced by an experimental comparison between the estimated population and the ground-truth population in 1 km grids.

Managing mixed waste poses environmental challenges and source separation has been encouraged worldwide. By reviewing 279 relevant papers systematically on waste separation behavior, its definition, influencing factors, commonly applied theories, statistical models, and implications are presented in this paper. Firstly, the term waste separation behavior was defined compared to similar concepts. Taking account of the multi-level attributes of separation behavior, the determinants were divided into micro and macro factors. Among the micro factors, the factors with the most empirical evidence are norms, intention, convenience, and knowledge. Personality and affective evaluation are suggested to be included for future investigation. On the macro level, four factors are analyzed: policies, economy, culture, and market. Possible pathways for research and interventions are given to encourage relevant stakeholders to understand and promote waste separation behavior.

Exploring the impact of technological innovation on carbon efficiency is conducive to achieving a better low-carbon transition and thus reaching sustainable development goals. However, the academic community still has no consensus about the impact of technological innovation on carbon efficiency. China is a vast country with differences in resource endowment and economic development in different regions. Therefore, this paper first assesses the level of technological innovation and carbon emission efficiency in China through panel data of 30 provinces in China. Then, the PVAR (Panel Vector Autoregressive) model is utilized to explore the regional differences in the impact of technological innovation on carbon emission efficiency. The findings reveal that: First, the level of technological innovation in China shows a continuous development trend, but the level as a whole is relatively low. Technological innovation in western China is far behind that in eastern China. Second, China’s carbon emission efficiency is generally at a high level but shows a trend from rise to fall. The efficiency of carbon emission in eastern China is higher than in central China, and the efficiency of carbon emission in central China is higher than in western China. Third, the impulse response reveals that the influence of China’s technological innovation on the efficiency of carbon emission has experienced a change from negative impact to positive impact. The initial influence of technological innovation on carbon emission efficiency is negative, with the largest negative impact seen in central China (−0.100), followed by the eastern area of China (−0.050), and finally the western region of China (−0.005). After one period, technological innovation turned to have positive effect on the efficiency of carbon emission, with eastern China having the most positive impact (0.060), followed by central China (0.010), and western China ranking last (0.001). The above findings have implications for the formulation of technological innovation policies in different regions of China to improve the efficiency of carbon emissions in accordance with local conditions.

The literature review reveals that lean manufacturing tool selection models still have some gaps. These models lack the criteria for selecting LM tools. Only a few of these models adopted hybrid multi-criteria decision-making (MCDM) methods. Obtaining reliable criteria weights in these models is complicated. They lack the consideration of grey uncertainty. Thus, this study is the first to propose a hybrid model for selecting a set of LM tools based on their effect on sustainability. This model combines the best-worst method (BWM) for weighting the criteria and the grey technique for order of preference by similarity to the ideal solution (Grey-TOPSIS) method to rank the alternatives and address the grey uncertainty problem. A set of sustainability metrics (selection criteria) was determined based on a literature review and expert evaluation to prioritize a set of LM tools. An Iraqi cement company was utilized to evaluate the proposed model. The ranking results showed that the value stream mapping (VSM) tool was the most important, whereas the single-minute exchange of die (SMED) tool was the least important. The rankings of the remaining LM tools ranged between these two tools depending on their effects on sustainability. The study conducted a sensitivity analysis using three strategies that verified the model’s robustness and reliability. This research provides 16 applicable sustainability metrics and 12 LM tools that could function as a knowledge foundation for future research. It can help researchers and manufacturers maximize sustainability performance by delivering a hybrid MCDM model to select the appropriate LM tools.

Climate change poses significant challenges to small-scale farmers in developing countries, who often have low adaptive capacity and capability. This study examines the factors influencing climate adaptation behaviors among small-scale sugarcane farmers in Indonesia. Using a multivariate probit model and data from a survey of 209 farm households, this study analyzes the association of climate risk behaviors, farmers’ support systems, and sugarcane–cattle integration with climate adaptation practices. The results reveal that farmers perceive climate change as a significant threat to sugarcane productivity, and their risk behaviors, such as climate risk perception and risk preference, influence their adaptation practices. The study also finds that sugarcane–cattle integration and farmers’ support systems, such as extension and training programs, farmers’ institutions, and information access, are crucial for farmers to adapt to climate issues. These findings can help policymakers design targeted and inclusive programs and strategies to support small-scale farmers in adapting to climate change.

The Sustainability Assessment of Food and Agriculture System (SAFA) is a multi-criteria sustainability assessment tool developed by the Food and Agriculture Organization in 2014. This study aims to contribute to the debate on multi-criteria sustainability assessments by applying the SAFA to a test case of an agricultural supply chain, including production, processing, distribution, and marketing. The study case of the maize monoculture value chain in the Mae Chaem District of Chiang Mai Province was selected as a typical highland maize monoculture in northern Thailand. The study area is the site of a rapidly expanding peasant farmer boom of maize production and global livestock feed industry. This qualitative research employs in-depth interviews and questionnaires of all participants along the value chain of the study area. Multiple social sustainability dimensions including decent livelihood, fair trading practices, labor rights, equity, human safety and health, and cultural diversity were assessed using the SAFA tool. The analysis results were moderately favorable in terms of social sustainability, which to a notable extent contrasts with sustainability issues surrounding maize monoculture in Northern Thailand. In terms of the social sustainability dimensions of fair trading practices and of decent livelihood, the results suggest that the contract farming system usually employed in the highland maize monoculture in northern Thailand is unsustainable. Finally, we discussed the limitations of the SAFA tool.

The carbon footprint of rural household consumption in China has a substantial scale and unique characteristics compared to urban areas. However, there remains a lack of studies that clarify the sources and potential of rural household carbon footprint in China. In this study, we estimated the rural household carbon footprint of 30 provinces in China’s mainland in 2007, 2012, and 2017 based on the Multi-Regional Input-Output model, and investigated the transition patterns with a consideration of the trends, regional differences, driving forces, and structural changes. Results revealed that the carbon footprint of rural household consumption in China grew by 83% from 2007 to 2017 and displayed a weak decoupling from income growth. The transition patterns were observed from three perspectives: Firstly, the primary driving force behind the growth was income increase, while the decrease in carbon footprint intensity slowed down the growth significantly. Secondly, housing and direct emission contributed to 62% of the growth in rural household carbon footprint, while health care, transportation, and other services showed increasing contributions. Lastly, there were notable “higher in the north, lower in the south” regional differences in the per capita rural household carbon footprint, and the gap tended to increase. The main reasons for the regional differences were intensity change, income increase, housing consumption, and direct emission. Our findings can offer decision-making support to guide rural household consumption towards achieving carbon peaking and carbon neutrality goals.

According to the US Environmental Protection Agency, around 11 million tons of post-consumer textile waste (PCTW) are disposed in U.S. landfills annually, which is 8% of all municipal solid waste. PCTW is landfilled because it contains complex blends of natural and synthetic fibers that are not easy to separate, and dyes and finishing chemicals on the fabrics interfere with recycling. The goal of this work was to develop a laboratory scale process for deconstructing and separating cut fabrics into different fiber fractions to create purified product streams that could promote textile recycling. Method parameters were selected from preliminary tests on various fabric types, followed by parametric evaluation with a set of rationally prepared model textile wastes. The combination of aggressive mechanical agitation together with cellulase catalyzed hydrolysis caused 100% cotton fabrics to disintegrate completely into a slurry of < 2 mm small solids and water soluble degradation products. The presence of reactive dyes on the model fabrics inhibited degradation, with the bifunctional reactive dye creating larger barriers to degradation than the monofunctional dye. Dye induced barriers were overcome with sufficient time, enzyme amount, and repeated treatment. Even though its collateral impact was a decrease in initial fabric burst strength, the presence of durable press (DP) finish on cotton presented a large obstacle to enzymatic degradation. This was overcome by including acid/alkali pretreatments to DP fabric before applying enzyme. The presence of polyester fiber in a cotton/polyester blend caused the fabric to retain its macroscopic knitted structure, while enzymatically degraded cotton was removed by washing and filtration to yield clean polyester. In all cases, fabric degradation products were separated by filtration into – depending on the severity of the treatments – residual large solids and small solids fractions and a clarified process liquid that contained soluble components. These three fractions were quantified gravimetrically and were characterized using high-performance liquid chromatography (HPLC), x-ray diffraction (XRD), differential scanning calorimetry (DSC), Fourier-transform infrared spectroscopy (FTIR), viscometry, scanning electron microscopy (SEM) and optical microscopy. The small solids present in the slurries after cotton degradation could be valuable as additives for paper, composites and other products, while the glucose-rich process syrups could be used to produce fuels and chemicals by fermentation, all of which would help divert PCTW from landfills. Importantly, even when cellulosic textile components were not fully degraded to soluble compounds, their conversion to pumpable slurries enabled easy handling of the degraded material and allowed recovery of non-degraded synthetic fibers by simple filtration and washing.