Towards a clean production by exploring the nexus between agricultural ecosystem and environmental degradation using novel dynamic ARDL simulations approach

The ecological impact of agricultural production on CO2 emissions in India: Pathways to sustainable agriculture

This study examines the relationship between CO2 emissions and agricultural production in India from 1990 to 2023, using an Autoregressive Distributed Lag (ARDL) model. Key agricultural indicators analyzed include the Food Production Index (FPI), Cereal Production (CP), Livestock Production Index (LPI), and the value added by Agriculture, Forestry, and Fishing (AFF). The results show that on the long run, a 1 % increase in FPI leads to a 7.86 unit increase in CO2 emissions per capita, while a 1 % increase in livestock production results in a 3.28 unit decrease in CO2 emissions per capita. In the short run, a similar increase in food production and livestock production also influences CO2 emissions, with notable but varying impacts over time. These findings underline the environmental trade-offs between food security and CO2 emissions, emphasizing the need for sustainable agricultural practices. This research contributes to existing literature by utilizing a broad set of agricultural indicators and robust ARDL analysis to examine both short- and long-term effects, providing a more comprehensive understanding of agricultural sustainability. The study was prompted by India's rapid agricultural growth, driven by its growing population and economic expansion, which has raised significant environmental concerns. Unlike prior research that often takes a generalized or global approach, this study offers an India-specific analysis that captures the country's distinct socio-economic and ecological conditions. By focusing on nationally relevant agricultural indicators and sustainability challenges, the research provides context-sensitive insights that can support effective and targeted policy design. The findings highlight the importance of policies that align agricultural productivity with sustainability, supporting the UN Sustainable Development Goals on climate action and food security.

Energy security and its determinants in New Zealand

New Zealand relies on imported fossil fuels for about 38% of its primary energy. The country's energy demand is expected to grow due to population and economic growth, which will put more pressure on the energy system. Besides, resource scarcity, energy price volatility, and environmental challenges have made energy security a major concern for New Zealand and other countries. Given the lack of significant research on the effects of energy security factors in New Zealand, this study aims to shed light on the primary determinants of energy security using the dynamic autoregressive distributed lag method based on time series data from 1978 to 2021. The study found that a long-run link exists between energy security and energy intensity (energy efficiency), renewable energy use, fossil fuel consumption, and global oil prices. Real GDP, renewable energy consumption, and energy security were found to improve energy security, while fossil fuel consumption and world oil prices had a negative impact. The study also revealed a one-way causality from real GDP, fossil fuel consumption, and renewable energy use to energy security. In contrast, the relationship between energy intensity and energy security is bidirectional. Simulation results showed that global crude oil prices have a lower impact on energy security compared to other variables and are most responsive to a 5% shock in fossil fuel consumption, followed by economic growth.

The evaluation and optimization of the agricultural sustainable development based on a data-driven approach: A case from Northern Anhui

The sustainable advancement of agriculture stands as the fundamental cornerstone of sustainable human progress. This study introduces a data-centric methodological framework founded upon the holistic delineation of measurement, feature assessment, and pathway enhancement for agricultural sustainability. Initially, the research articulates a comprehensive evaluative schema incorporating sub-dimensions encompassing agricultural production, agricultural economics, the agricultural resource environment, and rural society, grounded in sustainable development theory. Subsequently, it devises a methodological apparatus for assessing and enhancing sustainable development capabilities, employing entropy evaluation methods and exploratory spatial data analysis techniques. Employing North Anhui as a case study, the viability of this approach is substantiated. The empirical inquiry conducted within this article operationalizes comprehensive evaluation and explores pathways for optimizing agricultural sustainability, focusing on the period spanning 2011 to 2020 in Northern Anhui. The findings affirm the feasibility and efficacy of the data-driven approach. Recommendations derived from the empirical exploration of agricultural sustainability pathways at the local level offer valuable insights for governmental authorities and policymakers. This research endeavor could be extrapolated to other geographical locales worldwide, fostering innovative strides in the sustainable development of regional agriculture.

Influence of university agricultural technology extension on efficient and sustainable agriculture

Agricultural extension, as an important part of modern agriculture, can promote the scientific transformation of the traditional agricultural production model. This paper analysed the impact of university agricultural technology extension on efficient and sustainable agriculture using difference-in-differences model (DID). The results showed that university agricultural technology extension plays a facilitating role by influencing the coordinated development and green development dimensions in efficient and sustainable agriculture; there is a moderating effect of modern agricultural industrial parked in university agricultural technology extension and efficient and sustainable agriculture; there are significant differences in the impact of university agricultural technology extension on efficient and sustainable agriculture across regions and different levels of development. The findings have important implications for evaluating the effectiveness of current university agricultural extension policies and how to further promote university agricultural extension. The study also established an evaluation index system for efficient and sustainable agriculture, explored the mechanism of university agricultural extension in promoting efficient and sustainable agriculture, and enriched relevant theoretical research.

Analyzing the impact of industrial growth and agricultural development on environmental degradation in South and East Asia

This study aims to examine the impact of industrial growth, agricultural value added, economic growth, and renewable energy consumption on environmental pollution in South and East Asian countries using panel ARDL approach over the period 1970-2020. Furthermore, the impact of institutional quality in this relationship is uniquely explored. Baseline model suggests that environmental damage is exacerbated by agricultural value added and industrial growth in the long run, but not in the short run. The relationship between economic growth and carbon emissions is an obvious inverted U-shaped link in the long run, but it is a U-shaped association in the short run. Renewable energy consumption has significant adverse effects on environmental pollution both in the short and long term. In the long run, institutional quality plays a stronger moderating role in the association between baseline regressors and environmental pollution. Also, institutional quality helps to expand the ability of industry and agriculture to improve environmental quality. Lastly, the threshold results reflect that the impact of regressors on environmental degradation is penetrating into the level of institutional quality. The strong progressive effects of agricultural and industrial growth on environmental degradation are more similar to the existing literature in the long term, but not in the short term. In addition, the long-term condensation effect of renewable energy on CO2 emissions and the enhancing effect of economic growth on CO2 emissions are relatively similar to existing literature. In contrast to weak institutional quality, strong institutions can improve the ability of agriculture and industry to reduce environmental damage.

Towards green economy and sustainable development in Bangladesh: assessing the role of social and environmental indicators

The evolution towards a green economy integrating social, economic, and environmental concerns has opened a new window to pursue the sustainable development goals (SDGs), especially for emerging nations. Nonetheless, despite being a pressing concern on a global scale, empirical research into the potential for green economy development in the context of Bangladesh has remained notably inadequate. To fill this void, this study is an attempt to evaluate the connection among economic growth, carbon dioxide (CO2) emissions, education, life expectancy, and technology to conclude the ecological and socio-economic repercussions of a green economy in Bangladesh's framework of achieving SDGs. Considering the statistical features of the annual data from 1990 to 2019, the autoregressive distributed lag (ARDL) method has been employed to analyze the connections between the chosen variables. The empirical outcomes show that an upsurge in CO2 is accompanied by a 3.66% increase in GDP over the long term, suggesting a positive and statistically significant relationship between the two variables. In addition, GDP increases by about 4.2% for every 1% increase in life expectancy. However, the relationship between technological innovation and education found an insignificant positive linkage with GDP. The most important takeaway from these findings is that the growth of Bangladesh's economy is occurring at the expense of the environment. Hence, this research recommends that, as a developing nation, Bangladesh should concentrate on environment-friendly alternatives, which can be done through the introduction of a green economy to achieve a sustainably developed economy.

Decarbonization through sustainable energy technologies: Asymmetric evidence from 20 most innovative nations across the globe

Since the discharge of carbon is one of the main causes for ongoing global warming issue and change in climate, most nations have committed to decarbonizing their economies at the COP26 summit. Thus, this investigation aims to explore the consequences of innovations in sustainable energy technologies on decarbonization in the 20 most innovative nations across the globe. In assessing the cause-and-effect relationship, we have used “Panel Non-linear Autoregressive Distributed Lag (P-NARDL)” technique. The findings demonstrated that the variables have a lasting relationship. The positive asymmetric shock in the innovations in sustainable energy technologies has a positive influence on the decarbonization of these nations, while the negative asymmetric effect is insignificant. According to the findings, clean energy negatively consequence on carbonization whereas growth in economy is favorably and considerably connected with it. The findings demonstrate that there is bidirectional causation between all variables under investigation, with the exception of the unidirectional causality flows from the usage of sustainable energy technology and emissions of CO2. In a global context, this research suggests that government should identify the roles of new sustainable energy technologies by reforming patenting regulations to rectify the environmental damages.

Impact of green technological innovations on environmental quality for Turkey: evidence from the novel dynamic ARDL simulation model

The contribution of this research is to provide empirical evidence that investing in green technology innovation (GTI) can reduce the ecological footprint in Turkey, which can lead to sustainable economic growth and environmental quality. The research also highlights the importance of controlling energy consumption, GDP, trade openness, and urbanization, as these variables have a positive or negative effect on ecological footprint. The findings of this research can be useful for the Turkish government, policymakers, and environmentalists to promote the implementation of GTI and eco-friendly resources, which can reduce the impact of climate change and contribute to economic prosperity. Overall, this research provides important information for decision-makers to adopt policies that prioritize green innovation and environmental protection in Turkey.

Evaluating Indirect Economic Losses from Flooding Using Input-Output Analysis: An Application to China's Jiangxi Province

Quantifying total economic impacts of flood disaster in a timely manner is essential for flood risk management and sustainable economic growth. This study takes the flood disaster in China's Jiangxi province during the flood season in 2020 as an example, and exploits the input-output method to analyze indirect economic impacts caused by the agricultural direct economic loss. Based on regional IO data and MRIO data, a multi-dimensional econometric analysis was undertaken in terms of inter-regional, multi-regional, and structural decomposition of indirect economic losses. Our study reveals that the indirect economic losses caused by the agricultural sector in other sectors in Jiangxi province were 2.08 times the direct economic losses, of which the manufacturing sector suffered the worst, accounting for 70.11% of the total indirect economic losses. In addition, in terms of demand side and supply side indirect losses, the manufacturing and construction industries were found to be more vulnerable than other industries, and the flood disaster caused the largest indirect economic loss in eastern China. Besides, the supply side losses were significantly higher than the demand side losses, highlighting that the agricultural sector has strong spillover effects on the supply side. Moreover, based on the MRIO data of the years 2012 and 2015, dynamic structural decomposition analysis was undertaken, which showed that changes in the distributional structure appear to be influential in the evaluation of indirect economic losses. The findings highlight the spatial and sectoral heterogeneity of indirect economic losses caused by floods, and have significant implications for disaster mitigation and recovery strategies.

Perception of and adaptation to climate change: the case of wheat farmers in northwest Bangladesh

Climate change's impact on crop production is a global concern. A better understanding of farmers' perceptions of climate change and adaptation strategies will benefit farmers and policymakers in outlining an effective adaptation mechanism to climate change. Therefore, this study assessed wheat farmers' perceptions of climate change, identified major adaptation strategies, factors influencing adaptations, and barriers to effective adaptation by surveying 160 wheat farmers in northwest Bangladesh. The results revealed that farmers experienced more frequent droughts due to higher temperatures, decreased and irregular precipitation, reduced ground and surface water availability, and shorter winter seasons over the last two decades. Key adaptation strategies identified were more irrigation, switching to other crops, and changing fertilizer and insecticide usage. Multinomial logit model results indicate that farming experience, access to climate information and extension services, access to subsidies, farm size, family size, and electricity for irrigation were the significant factors influencing farmers' adaptation decisions. Limited access to climate information, inadequate knowledge of appropriate adaptation measures, and low price of wheat represented major adaptation barriers. The study recommends strengthening agricultural research and extension services to farmers, including education and training, to develop effective adaptation strategies to climate change.

Do farmers use waterlogged wastelands efficiently? An economic study on water chestnut farming in Bangladesh

Waterlogging due to rain-fed floods is considered a natural calamity worldwide that causes lands in the north region of Bangladesh to remain underwater and uncultivable for most crops during the rainy season. This unused wasteland has immense potential for additional earnings for the marginal farmer by converting it to cultivable land through proper utilization by cultivating water chestnuts. Increasing the productivity and efficiency of water chestnut farming in these wastelands would facilitate higher food production for the growing populations. Therefore, this study estimates the farmers' profitability of water chestnut production along with technical efficiency (TE) and land use efficiency (LUE). Primary data from 150 farmers of Natore, Naogaon, and Jamalpur districts were used and stochastic frontier analysis was employed. Results reveal that no farmers had any training in water chestnut farming. Human labor and land use costs incur more than 80% of the total cost and laborers shortage was found due to skin problems working in the water. However, water chestnut farming was profitable in all districts and the average benefit-cost ratio was 1.37. TE results indicate that there was an opportunity to increase the water chestnut production by 20.2% using the same amount of inputs. LUE by the water chestnut farmers was found to be very low; hence, they were using their land inefficiently and this inefficiency was positively affected by farm size and number of family members and negatively affected by age, year of schooling, and income. The study suggests research focusing on improved management of water chestnut without affecting laborers and training to facilitate higher productivity and LUE.

Addressing the effect of meteorological factors and agricultural subsidy on agricultural productivity in India: a roadmap toward environmental sustainability

In the last two decades, the extensive literature that has measured agricultural productivity and growth rate remains controversial and provides few strategies about its main determinants. The present study aims to find out the key determinants of food grain yield (FGY) and examine the role of climate change and agricultural subsidy (SUB) in the context of India using annually data spanning from 1991 to 2018. The current study applied the ARDL modelling to investigate the impacts of climatic factors (average rainfall (RF), mean temperature (AT), and carbon emission (CO₂) and agricultural subsidy (SUB) on food grain yield (FGY) in the short-and long-term in India. The estimated outcomes indicate that climatic factors such as RF have a positive impact while AT and CO₂ have a negative effect on FGY. Similarly, non-climate variables such as gross capital formation (GCF) and fertilizer usage (FERT) positively contributed to FGY, while the area under crop (LUC), SUB, and employment (AL) negatively affected FGY in India. The results from Granger causality divulge that climatic and non-climatic elements are the main determinants of food grain yield, which have been playing play a significant role in enhancing food grain production and ensuring food security in India. Based on empirical outcomes and findings, some key policy implications emerged. Precisely, government and policy developers should focus on technological innovation and precision agriculture to increase agriculture production and productivity. Government should create funds to curb the climate change problem and promote eco-friendly renewable energy.

Environmental Impact Assessment for Animal Waste, Organic and Synthetic Fertilizers

The use of fertilizers is of the utmost importance for food security on a global scale. However, fertilizer production and overuse may yield environmental issues. In this research, Life Cycle Assessment (LCA) was used to estimate eighteen environmental impact categories for six different fertilizer products: three synthetic (ammonium nitrate; calcium ammonium nitrate; and urea ammonium nitrate) and three organic (cattle manure; compost; and a mixture of compost and synthetic fertilizer). The processes for fertilizer production were obtained from the Agribalyse database. The system boundaries were from cradle to factory gate (or farm gate in the case of animal waste), and the impact indicators were calculated per kg of nitrogen (N). The data showed that the organo-mineral fertilizer (a mix of compost and synthetic fertilizer) had the highest environmental impact according to the results for most of the impact categories. The median values for this product regarding water consumption, fossil resource use and global warming potential were 322.5 L, 3.82 kg oil equivalent and 13.70 kg CO2 equivalent, respectively, per kg of N. The respective values for cattle manure, for which the lowest environmental impact was observed, were 0.23 L of water, 0.002 kg oil-eq and 3.29 kg of CO2-eq, respectively, per kg of N. Further research should focus on the determination of the impact from other stages of the life cycle (e.g., transportation and application to the field) which were not included in this work. This research could support the selection of N fertilizer in sustainable food production.

Energy mix with technological innovation to abate carbon emission: fresh evidence from Mexico applying wavelet tools and spectral causality

The global warming issue arises from climate change, which draws scientists' attention toward cleaner energy sources. Among clean sources, renewables and nuclear energy are getting immense attention among policymakers. However, the significance of nuclear energy in reducing CO₂ emissions has remained ambiguous, necessitating further research. Therefore, the present study draws impetuous attention to the United Nations Sustainable Development Goals-7 (affordable clean energy) & 13 (climate change mitigation) by looking at the relationship between energy mix (fossil fuels, renewables, and nuclear), economic growth, technological innovation, and CO₂ emissions in Mexico from 1980 to 2019 using the autoregressive distributed lag (ARDL) model. In addition, to assess the direction of causality, this study applied wavelet techniques and spectral causality. The findings affirm that renewable and nuclear energy use and technological innovation tend to curb CO₂ emissions, whereas fossil fuel consumption and economic expansion trigger CO₂ emissions. The study lends support to the environmental Kuznets curve (EKC) phenomenon in Mexico. The FMOLS and DOLS tests show that our long-run estimates are reliable. In different time scales, the wavelet coherence result is also consistent. Finally, the results of the spectral causality approach demonstrate a significant causal association between the variables tested at various frequencies. As a result, in order to achieve SDGs 7 and 13 and support an environmentally friendly ecosystem, Mexico's energy mix must be changed to renewables and nuclear.

Can Renewable Energy and Export Help in Reducing Ecological Footprint of India? Empirical Evidence from Augmented ARDL Co-Integration and Dynamic ARDL Simulations

The objective of this study is to investigate the impact of exports, renewable energy, and industrialization on the ecological footprint (EF) of India over the period spanning from 1970–2017 by employing the newly developed augmented ARDL (A-ARDL) co-integration approach and the novel dynamic ARDL (D-ARDL) technique. The empirical results demonstrate that exports and renewable energy consumption reduce the EF, while industrialization intensifies the EF. More precisely, a 1% increase in export (renewable energy consumption) reduces the EF by 0.05% (0.09%). In addition, the short-run elasticity of the GDP is found to be larger than the long-run elasticity indicating the possibility of the existence of the Environmental Kuznets Curve (EKC) of the EF for India. The study indicates that the income effect and increased policy focus on renewable energy usage can be expected to reduce India’s per capita EF in the long run. Moreover, India’s export sector has been traditionally less energy intensive, which reflects in our findings of export growth leading to a reduction in EF. Based on the empirical findings, this study recommends some policy insights that may assist India to effectively reduce its ecological footprint.

Linking shadow economy and CO2 emissions in Nigeria: Exploring the role of financial development and stock market performance. Fresh insight from the novel dynamic ARDL simulation and spectral causality approach

First and foremost, the present study seeks to traverse the informal sector characterized by a shadow economy in the presence of financial development, economic growth, and stock market performance on environmental pollution in Nigeria from 1981 to 2019. The dynamic autoregressive distributed lag (DARDL) approach was used to measure the short- and long-run elasticities, while spectral causality is applied to categorize the causal directions. Findings from the study revealed that the structural break unit root test revealed that all variables are stationary at first difference. The ARDL bound test confirmed the existence of long-run association among the used variables. The ARDL long-run results reveal that economic growth, financial development, and stock market performance are significantly responsible for carbon emission in Nigeria, while the shadow economy significantly improves environmental quality in Nigeria. Findings from the spectral causality results show a unidirectional causal relationship between financial development, economic growth, trade, stock market performance, and shadow economy to carbon emission in Nigeria. The empirical findings of this study provide some perceptive policy recommendations to overcome the adverse effect of carbon emissions in the environment.

Can Energy Efficiency Help in Achieving Carbon-Neutrality Pledges? A Developing Country Perspective Using Dynamic ARDL Simulations

The current research sheds light on the nexus between environmental degradation as proxied by carbon dioxide emissions (CO2), energy efficiency (EE), economic growth, manufacturing value-added (MVA), and the interaction effect of EE and MVA in India. Using yearly data from 1980 to 2019, the current study employs dynamic auto-regressive distribution lag (DARDL) simulations and Fourier Toda and Yamamoto causality techniques. The findings of DARDL reveal that as income and MVA rise, environmental quality decreases, while EE improves environmental conditions in both the long and short run. Surprisingly, the interaction term of EE and MVA has a detrimental influence on environmental quality, meaning that India remains unable to provide energy savings technologies to the manufacturing industry. Furthermore, the environmental Kuznets curve (EKC) hypothesis is well-founded for India, as the long-run income coefficient is smaller than the short-run coefficient, implying that India is in its scale stage of economy, where economic growth is prioritized over environmental quality. The results of the causality technique reveal that CO2 emissions and EE have a bidirectional association. Therefore, policymakers in India should embrace realistic industrialization strategies combined with moderate decarbonization and energy efficiency initiatives under the umbrella of sustainable industrial and economic growth.