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Abstract Theft of electricity is a problem in many developing countries. But AMI is paving the way for data-centric architecture to help in theft detection. However, a smart grid or even AMR is a long shot for many developing countries due to the costs involved in its large-scale deployment. This paper presents a technique to detect outliers among electricity users that further investigates electricity theft using data analytics on monthly usage data available to every utility company. Using this technique, we have reduced the search space for theft identification to as low as 3.4% of the total customer base.

Abstract Theft of electricity is a problem in many developing countries. But AMI is paving the way for data-centric architecture to help in theft detection. However, a smart grid or even AMR is a long shot for many developing countries due to the costs involved in its large-scale deployment. This paper presents a technique to detect outliers among electricity users that further investigates electricity theft using data analytics on monthly usage data available to every utility company. Using this technique, we have reduced the search space for theft identification to as low as 3.4% of the total customer base.

The global community has set intensive targets in Sustainable Development Goals (SDGs) to better people’s lives after closing the Millennium Development Goals (MDGs). It corresponds to the 2030 aspirations of the United Nations to enhance and promote the sustainable development of human society. The current paper explores the impact of fiscal hedging and R&D in energy Using a green-energy system in SDGs. To do this, we used TOPSIS and QARDL methodologies on a 21-year dataset of South and Southeast Asian economies from 2000 to 2020. The study results show that fiscal hedging contributes favourably to the environmental degradation of the underlying economy. Research and development (R&D) in renewables has contributed negatively to ecological degradation and SDGs in the economies of South & Southeast Asia. This study suggests policy guidelines for advanced and developing economies based on fiscal stability and technical innovation through R&D to meet SDG.

The global community has set intensive targets in Sustainable Development Goals (SDGs) to better people’s lives after closing the Millennium Development Goals (MDGs). It corresponds to the 2030 aspirations of the United Nations to enhance and promote the sustainable development of human society. The current paper explores the impact of fiscal hedging and R&D in energy Using a green-energy system in SDGs. To do this, we used TOPSIS and QARDL methodologies on a 21-year dataset of South and Southeast Asian economies from 2000 to 2020. The study results show that fiscal hedging contributes favourably to the environmental degradation of the underlying economy. Research and development (R&D) in renewables has contributed negatively to ecological degradation and SDGs in the economies of South & Southeast Asia. This study suggests policy guidelines for advanced and developing economies based on fiscal stability and technical innovation through R&D to meet SDG.

The upsurge in higher education is considered a key determinant for enhancing green growth. Moreover, ICT development is also the main catalyst of green growth. This research explores the role of higher education and ICT on green growth for China from 1995 to 2020. The study employs auto-regressive distributive lag (ARDL) approach for short-run and long-run estimates of green growth. The effect of higher education and ICT on green growth is significantly positive in the long run and short run. The outcomes of the empirical models reveal that financial inclusion is positively associated with green growth in both long run and short run. Moreover, renewable energy consumption is found to have a positive impact on green growth. The findings thus point to the need for policies that promote human capital and ICT infrastructure as a way of accelerating green growth.

The upsurge in higher education is considered a key determinant for enhancing green growth. Moreover, ICT development is also the main catalyst of green growth. This research explores the role of higher education and ICT on green growth for China from 1995 to 2020. The study employs auto-regressive distributive lag (ARDL) approach for short-run and long-run estimates of green growth. The effect of higher education and ICT on green growth is significantly positive in the long run and short run. The outcomes of the empirical models reveal that financial inclusion is positively associated with green growth in both long run and short run. Moreover, renewable energy consumption is found to have a positive impact on green growth. The findings thus point to the need for policies that promote human capital and ICT infrastructure as a way of accelerating green growth.

The relationship between exchange rate volatility (ERV) and other macroeconomic factors, including trade flows, domestic production, inflation, money demand, and economic growth, has remained a topic of a large number of studies in international finance. However, the research question that the past empirics have overlooked is whether ERV has any role in helping an emerging economy like China in its journey toward a renewable energy transition. To answer this question, this analysis intends to scrutinize the nexus between ERV and renewable energy investment (REI) in China over 1991Q1-2021Q4. Moreover, due to the asymmetric behavior of ERV, we have based our analysis on the asymmetric assumption. For analyzing the short and long-run impacts of ERV on REI in China, we used the linear autoregressive distributed lag (ARDL) and nonlinear ARDL methods. In the long term, the ARDL model predicts that stricter environmental policies and higher GDP will lead to more investment in renewable energy. As far as the nonlinear model is concerned, the long-term negative change in ERV does not affect REIs, whereas a long-term positive change in ERV reduces such investments. Likewise, unfavorable short-term exchange rate shocks encourage REI while positive short-term shocks discourage such development. Moreover, investment in renewable energy is bolstered by GDP, environmental policy strictness, and financial development, but is dampened by CO2 emissions in the short term in both models. On the basis of these results, we suggest that policymakers should consider implementing measures to stabilize exchange rates to promote investment in renewable energy.

The relationship between exchange rate volatility (ERV) and other macroeconomic factors, including trade flows, domestic production, inflation, money demand, and economic growth, has remained a topic of a large number of studies in international finance. However, the research question that the past empirics have overlooked is whether ERV has any role in helping an emerging economy like China in its journey toward a renewable energy transition. To answer this question, this analysis intends to scrutinize the nexus between ERV and renewable energy investment (REI) in China over 1991Q1-2021Q4. Moreover, due to the asymmetric behavior of ERV, we have based our analysis on the asymmetric assumption. For analyzing the short and long-run impacts of ERV on REI in China, we used the linear autoregressive distributed lag (ARDL) and nonlinear ARDL methods. In the long term, the ARDL model predicts that stricter environmental policies and higher GDP will lead to more investment in renewable energy. As far as the nonlinear model is concerned, the long-term negative change in ERV does not affect REIs, whereas a long-term positive change in ERV reduces such investments. Likewise, unfavorable short-term exchange rate shocks encourage REI while positive short-term shocks discourage such development. Moreover, investment in renewable energy is bolstered by GDP, environmental policy strictness, and financial development, but is dampened by CO2 emissions in the short term in both models. On the basis of these results, we suggest that policymakers should consider implementing measures to stabilize exchange rates to promote investment in renewable energy.

Agriculture is the largest sector of Pakistan’s economy, contributing almost 22% to the GDP and employing almost 45% of the total labor force. The two largest food crops, wheat and rice, contribute 3.1% and 1.4% to the GDP, respectively. The objective of this research was to calculate the energy return on investment (EROI) of these crops on a national scale from 1999 to 2009 to understand the size of various energy inputs and to discuss their contributions to the energy output. Energy inputs accounted for within the cropping systems included seed, fertilizer, pesticide, human labor, tractor diesel, irrigation pump electricity and diesel, the transport of fertilizer and pesticide, and the embodied energy of tractors and irrigation pumps. The largest per-hectare energy inputs to wheat were nitrogen fertilizer (52.6%), seed (17.9%), and tractor diesel (9.1%). For rice, the largest per-hectare energy inputs were nitrogen fertilizer (32%), tube well diesel (19.8%), and pesticide (17.6%). The EROI of wheat showed a gradual downward trend between 2000 and 2006 of 21.3%. The trend was erratic thereafter. Overall, it ranged from 2.7 to 3.4 with an average of 2.9 over the 11-year study period. The overall trend was fairly consistent compared to that of rice which ranged between 3.1 and 4.9, and averaged 3.9. Rice’s EROI dipped sharply in 2002, was erratic, and remained below four until 2007. It rose sharply after that. As energy inputs increased, wheat outputs increased, but rice outputs decreased slightly. Rice responded to inputs with greater output and an increase in EROI. The same was not true for wheat, which showed little change in EROI in the face of increasing inputs. This suggests that additional investments of energy in rice production are not improving yields but for wheat, these investments are still generating benefits. The analysis shows quantitatively how fossil energy is a key driver of the Pakistani agricultural system as it traces direct and indirect energy inputs to two major food crops.