Environment, Development and Sustainability最新文献

Energy production and recycling from chicken manure (CM) is a sustainable and environmentally friendly approach. The CM is considered a valuable bioresource for methane production. The commercial applications of colimoxin for the poultry bird health; its traces are being ejected in CM and it becomes toxic for the methanogenic archaea during the anaerobic digestion process. The influences of the colimoxin were investigated on the anaerobic co-digestion performance of the CM and wheat straw (WS). The CM is co-digested with WS at carbon to nitrogen ratio of 25 to reduce the nitrogen contents of the CM. To optimize the colimoxin dose for methane generation; eight different colimoxin treatments were applied with varying colimoxin dose. The lower colimoxin dose was found healthy for the microbes and Treatment-4 resulted in 23% methane enhancement, while the higher colimoxin doses inhibited the methane production. The synergistic influences of the colimoxin dose on the volatile fatty acids generation and utilization were deeply monitored. The regression models were developed by using second degree polynomial regression models to optimize the colimoxin dose for the commercial biogas facilities and for the commercial poultry control sheds. The onsite methane generation would result in in-situ electrical energy production for the rural community.

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Alternative fuels in internal combustion engines have gained significant attention to environmental sustainability and energy security. The study employs a machine-learning (ML) approach, integrating artificial neural networks (ANN) and response surface method (RSM), to analyze the engine characteristics. The experimental data used to train the ANN and RSM model was obtained by employing different combinations of input parameters obtained by the Design of the experiment tool. Four input parameters load 25–100% ((1.3, 2.6, 3.9, and 5.2 kW) loading condition, speed (1200, 1500, and 1800 RPM), compression ratio (17.5 and 18.5), and biodiesel–diesel blends (Diesel, SM₂₀, SM₄₀, SM₆₀, SM₈₀ and SM₁₀₀) were used. The results show predictability for ANN with training and test regression coefficients (R²) of 0.975 and 0.948 whereas RSM with R² of 0.992. Optimized results for RSM and ANN, BTE (29.4% and 29.1%), BSFC (0.0.3201 and 0.334 kg/kWh), IMEP (2.83 and 2.69 bar), and CO₂ (922.72 and 940.87 g/kwh), NOx (964 and 937 ppm). When compared with experimental data, the error was about 5%. It can be inferred that RSM and ANN may be used to model processes with high predictability and that optimization can be carried out using various techniques depending on the process or problem.

Given the severe global warming trend, analyzing carbon emissions causes is vital for crafting emission reduction policies. Countries prioritize sustainable development by shifting development modes, regulating population, and fostering renewable energy. Due to disparities in political, economic, and resource aspects among countries, the specific measures taken are various. From an academic perspective, clarifying carbon emission drivers and exploring deep-level factors becomes imperative. Existing research examines carbon emission factors through driver decomposition and analysis of factors influencing energy intensity and economic development. However, these studies operate independently, lacking systematic integration. Therefore, based on 159 articles on driver decomposition and 106 articles on drivers’ influencing factors, this paper uses systematic review and Meta-analysis to explore deep-level factors of carbon emission. The results find that economic development and energy intensity are the main drivers of carbon emission changes across countries, with median contributions ranging from 0.02 to 0.1 and − 0.05 to 0. Energy prices, technological innovation, service industry share, and financial development notably curb energy intensity and bolster economic development, ranging from 0.181 to 0.777 and 0.068 to 0.202, as the deep-level factors for carbon reduction. Urbanization, industrialization, gross capital formation, and fossil energy share significantly promote energy intensity and economic development, ranging from 0.105 to 0.216 and 0.044 to 0.286, as the deep-level factors for increasing carbon emissions. High-income countries’ service industry has a more adverse effect on carbon emissions than lower-income countries, and urbanization has a greater impact on emissions in lower-income countries. This study provides insights for global low-carbon development.

Watershed ecological compensation (WEC) aims to enhance watershed environmental protection and ecological security. However, existing studies often ignore the integrity of large-scale watershed and regional heterogeneity in economic development when determining compensation standards and allocating funds, which makes the implementation of WEC policies difficult to achieve the desired goals. This study adopts the ecosystem services value (ESV) method to quantify the ESV changes using land use data of the Yangtze River Basin (YRB) from 2005 to 2020. On this basis, a WEC standards measurement model and a funds optimization allocation model are constructed to determine the theoretical compensation standards and actual funds allocation for eleven regions in the YRB. The results show that: (1) The land-use structure tended to be stable on the whole, with forest and grassland area exceeded 60% of the total. (2) The total ESV in the YRB exhibited a gradual decline from 16,983.19 billion CNY in 2005 to 16,333.36 billion CNY in 2015, followed by a marked increase to 17,520.45 billion CNY in 2020. (3) The theoretical compensation amounts of the entire YRB were 23.86 billion CNY in 2005, 23.46 billion CNY in 2010, 23.13 billion CNY in 2015 and 24.55 billion CNY in 2020 respectively. (4) The actual compensation funds received by the districts in the YRB were all below the theoretical WEC standards as measured by ESV outputs (35.94%, 52.22%, and 83.36%, respectively). In 2020, there was a reversal, with the actual funds reaching 1.33 times the theoretical standard. This study can provide references for governments to construct WEC programs and improve the efficiency of compensation funds applicable to large-scale watersheds.

Sustainable agriculture is gaining importance amid the shift to Agriculture 5.0, highlighting the need to evaluate the sustainability aspects of precision agriculture devices. In this study, the sustainability of FarmFox has been explored and compared with existing devices using AHP considering four use cases. A sustainability index has been developed to improve any ambiguity imposed by AHP ratings. The sustainability index value for FarmFox ranged between 4.7 and 4.85, indicating a high level of sustainability. In contrast, the other two devices demonstrated sustainability index values within the ranges of 4.0 to 4.2 and 1.31 to 1.34, reflecting moderate to strong sustainability levels, respectively. The two successful applications of FarmFox in small- and large-scale system respectively showcases its robustness. The proposed sustainable solution framework highlights four out of five aspects of sustainable farming crop management, soil management, water conservation and disease management. Furthermore, the SWOTC reveals that data privacy and data loss are the two major threats whereas dealing with power supply disruption and RTC synchronization was found to be the major challenges. It is expected that with the growing trend of urban farmers, prototypes such as FarmFox ensure the paradigm shift towards Agriculture 5.0. With commercialization of FarmFox at an affordable cost will ensure agricultural sustainability in line with the sustainable development goals.

Seaweed export from Bangladesh holds significant potential for economic growth and export diversification. Cluster-based blue entrepreneurship emerges as a promising strategy to bolster seaweed exports. This study explores the determinants of seaweed export propensity in Bangladesh, drawing on data from a survey of 233 seaweed farmers using non-probability snowball sampling. The collected data were analysed using partial least square structural equation modelling (PLS-SEM) due to the complex nature of the research model. Key factors examined include cluster-based blue entrepreneurship, blue technology penetration, institutional assistance, mimetic pressure, and institutional voids. The findings indicate that cluster-based blue entrepreneurship positively influences export propensity. However, the study does not find substantial support for the relationship between cluster-based blue entrepreneurship and blue technology penetration. Notably, institutional assistance, mimetic pressure, and institutional voids play pivotal roles in moderating the impact of blue technology penetration on export propensity. Moreover, the study underscores the beneficial impact of blue technology penetration on export propensity and identifies institutional voids as critical moderators. It highlights the necessity for supportive institutional frameworks to foster cluster-based blue entrepreneurship and enhance export potential. These insights are crucial for policymakers, industry stakeholders, and practitioners aiming to formulate strategies for sustainable growth in Bangladesh’s seaweed farming sector. It advocates for targeted policy interventions that strengthen institutional support, mitigate mimetic pressures, and address voids to enhance the industry’s competitiveness and export potential. These insights offer practical implications for policymakers, industry stakeholders, and practitioners aiming to foster resilient and inclusive economic development in emerging marine resource sectors.

Carbon emission reduction is a critical objective for enhancing ecological and environmental quality. The shift toward green and sustainable practices is becoming increasingly central to the future development of data centers. Despite its importance, few studies have examined the impact of green data centers on carbon emissions. Based on the event of green data center pilots at district-county level in China, this paper explores the impact of green transformation of data centers on corporate carbon emission reduction and its mechanism of action by using a high-dimensional fixed-effects model with the help of a panel data of A-share listed companies in Shanghai and Shenzhen, China, from 2008 to 2021. The findings reveal: (1) The green transformation of data centers significantly promotes corporate carbon emission reduction. This result is robust, persisting even after adjusting for the influence of other policies and benchmark variables. (2) The study identifies that green transformation substantially enhances the level of breakthrough innovation within enterprises, which in turn significantly reduces their carbon emissions. Additionally, the level of green concern within a company positively moderates the relationship between green transformation and carbon emission reduction. (3) Heterogeneity analysis indicates that the effects of data center green transformation on carbon emissions vary significantly between central and western regions and non-interprovincial border areas. This research provides empirical evidence and policy recommendations to assist developing countries in balancing economic development with carbon emission reduction objectives.

Food production systems are faced with increasingly emerging pressures. Worldwide affairs like the Russia-Ukraine war and Covid-19 have raised our concerns about the ability to maintain a steady food supply at a stable price. Food security remains a problem to be addressed, especially taking the growing global population into consideration. This study aims to contribute to global food security by exploring the coupling relationship between resilience and sustainability of China’s food production system. An evaluation system to measure the elasticity and sustainability of China’s food production system was established, and the comprehensive evaluation value, coupling coordination degree, coupling coordination degree and coupling coordinated development type were quantitatively analyzed using entropy evaluation, comprehensive evaluation index model, coupling coordination model and related development level model. The results of our study are as follows. First, the level of resilience and sustainability of China’s food production system fluctuates and is generally on the rise. Second, the coordination level of internal coupling between the resilience and sustainability of China’s food production system is generally on the rise, but the degree of coupling coordination is still at a low level in some years. Third, in terms of comparative development, the resilience of China’s grain production system lagged behind its sustainability, and it only reached a state of synchronous development in 2019. The research findings will provide guidance to the adaptation between the resilience and sustainability of the Chinese food production system and inspire the formulation of related policies.

In the contemporary world, authorities are becoming more conscious of the growth that prioritizes sustainability. This can be achieved through the use of green technology in the production process. In this context, unlike other studies, this study examines the environmental quality of G-20 countries in terms of their ecological footprint (EFP) concerning environmental technological innovation and green growth indicators (i.e., green energy, economic growth, and green production). In this study, the “continuously updated fully modified (CUP-FM)” approach was conducted using the panel data from 1992 to 2021. Besides, this study also employed the “Dumitrescu & Hurlin (2012) panel causality tests” to find the causality relationship between the variables. According to the findings, a 1% increase in green energy supply leads to a 0.09% increase in EFP; therefore, dwindling overall ecological sustainability which is a surprising result for the G-20 context. Similarly, 1% increases in green innovation and green production have also been observed to increase EFP by 0.14% and 0.11%, respectively. Likewise, it was concluded that a 1% increase in economic growth increases EFP by 0.75%. The aforementioned conclusions suggest that the G-20 countries should give priority to projects related to the development of green technology, the application of strategies for sustainable economic growth, and the improvement of production efficiency to improve environmental quality.

The goal of this study is to thoroughly analyze the connection between green finance (GFI) and agricultural high-quality development (AGHID), with a focus on the use of financial technology (FinTech). This project’s objective is to offer a strong framework of reference for the promotion of agricultural modernization as well as the encouragement of AGHID. Using data from interprovincial panels between 2011 and 2019, this study investigates the role of FinTech and uses the generalized method of moments model to show the effect of GFI on the AGHID. The study's findings indicate that advances in GFI have a statistically significant effect on the AGHID, with significance levels at least reaching the 1% mark. FinTech’s contribution to the area of GFI concerning the AGHID is typically underappreciated. A 1% upswing in the level of GFI corresponds to an average 18.9% increase in the level of AGHID. The study investigates local discrepancies and reveals subtle differences in the ways that GFI influences the AGHID in different regions. All regions benefit from the AGHID when it comes to GFI, although it is most noticeable in the western region. A temporal analysis demonstrates that throughout the 13th period of the Five-Year Plan, the positive impact of GFI on the expansion of premium agriculture was clearly increasing. Further analysis reveals that the information channel impact and the human capital effect are crucial channels via which GFI contributes to the AGHID, providing further insight into the working processes.