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Abstract Under the low-carbon environment of economy and society, the real decoupling between economic growth and high carbon emissions is the basis for realizing the sustainable and low-carbon transformation and development of Shandong Province. This paper uses energy consumption as an intermediate variable, according to EKC hypothesis theory and Tapio decoupling indicator theory, by constructing the multiple regression model and low-carbon decoupling model of carbon emissions, energy consumption and economic growth, this makes an empirical analysis of the interval elasticity relationship and the point-in-time elasticity relationship in 1994−2017. The decoupling situation is: there is an inverted "N" curve relationship between carbon emissions and economic growth in Shandong Province. The second theoretical turning point is in 2012, and the decoupling relationship will gradually appear in the future. To a great extent, Shandong Province's low-carbon decoupling indicator is affected by the energy conservation decoupling indicator, and the strong decoupling state of economic growth and carbon emissions has appeared continuously, but it is not stable enough. The ideal decoupling state between economic growth and carbon emission will occur only after carbon emission reaches the peak, so it is necessary to predict the peak year of carbon emission. Based on the scenario analysis and decoupling theory, a carbon emission model is constructed to analyze the carbon emission status and its possible peak years in different scenarios in Shandong Province from 2016 to 2030. On the whole, with the improvement of economic development level, the situation of low energy consumption and low emissions is gradually significant. The degree of decoupling is mainly affected by the macro-economic situation and the change of policy regulation. Therefore, the paper puts forward the scientific, feasible and effective policy measures with Shandong characteristics to promote the sustainable development of low-carbon economy.

The current and projected warming of the earth is unequivocal with humans playing a strong role as both perpetrators and victims. The warming on the African continent is projected to be greater than the global average with an increased average temperature of 3–6°C by the end of the century under a high Representative Concentration Pathway. In Africa, the Sub-Saharan region is identified as the most vulnerable to the changing climate due to its very low capacity to adapt to or mitigate climate change. While it is common to identify studies conducted to assess how climate change independently impacts water, land, or food resources, very limited studies have sought to address the interlinkages, synergies, and trade-offs existing between climate change, water, land, and food (WLF) resources as a system in Sub-Saharan Africa (SSA). The climate change and WLF security nexus, therefore, seeks to address this shortfall in literature and subsequently serve as a relevant source of information for decision-making and policy implementation concerning climate change mitigation and adaptation. In this study, 41 relevant studies were selected from Web of Science, Google Scholar, ResearchGate, and institutional websites. We provide information on the independent relationships between climate change and WLF resources, and further discuss the existing inter-linkages between climate change and the WLF security in SSA using the nexus approach, with recommendations on how decision making and policy implementations should be done using the climate change and WLF security nexus approach.

Food insecurity is a growing concern due to man-made conflicts, climate change, and economic downturns. Forecasting the state of food insecurity is essential to be able to trigger early actions, for example, by humanitarian actors. To measure the actual state of food insecurity, expert and consensus-based approaches and surveys are currently used. Both require substantial manpower, time, and budget. This paper introduces an extreme gradient-boosting machine learning model to forecast monthly transitions in the state of food security in Ethiopia, at a spatial granularity of livelihood zones, and for lead times of one to 12 months, using open-source data. The transition in the state of food security, hereafter referred to as predictand, is represented by the Integrated Food Security Phase Classification Data. From 19 categories of datasets, 130 variables were derived and used as predictors of the transition in the state of food security. The predictors represent changes in climate and land, market, conflict, infrastructure, demographics and livelihood zone characteristics. The most relevant predictors are found to be food security history and surface soil moisture. Overall, the model performs best for forecasting Deteriorations and Improvements in the state of food security compared to the baselines. The proposed method performs (F1 macro score) at least twice as well as the best baseline (a dummy classifier) for a Deterioration. The model performs better when forecasting long-term (7 months; F1 macro average = 0.61) compared to short-term (3 months; F1 macro average = 0.51). Combining machine learning, Integrated Phase Classification (IPC) ratings from monitoring systems, and open data can add value to existing consensus-based forecasting approaches as this combination provides longer lead times and more regular updates. Our approach can also be transferred to other countries as most of the data on the predictors are openly available from global data repositories.

Environmental justice (EJ) has become an increasingly significant issue for environmental management and has thus attracted increasing government and public attention. Although some studies have used techniques of proximity based on geographical information systems to assess EJ, their research is limited to individual or household data. Unlike the conventional hedonic price model (HPM) examining the effects of environmental features on housing rent, this article incorporates counterfactual decomposition into the HPM to estimate the environmental pressure on different groups by comparing the externality effects of municipal solid waste treatment facilities (MSWTFs) on two separate groups of people. To explore whether and, if so, the extent to which, vulnerable groups of people are restricted to disproportionate impacts of hazardous environmental facilities, this research uses Shanghai as the study area to highlight specific locations and exemplify the environmental injustice between the rich and the poor. The results, which represent the relationship between environmental quality and property prices, indicate that environmental quality is a robust predictor of housing rent. Simultaneously, the results suggest that some people conform better to environmental pressure than do others. Thus, the environmental impact of MSWTFs on different populations should be considered, and compensation policies should be implemented for disadvantaged groups.

Abstract Industry and agriculture in Malaysia are the main contributors to economic growth and employment. These sectors also play an important role in Malaysia's total exports. The question then is whether technological innovation, sectoral output, and exports growth have had a real impact on these two sectors, which are very important for policy-making. This paper attempts to empirically identify such relations using econometric methods, including an autoregressive distributed lag (ARDL) bounds testing method and a dynamic ordinary least squares (DOLS) during 1978–2018. The results confirmed that overall long-run economic growth is the main contributor to the increase in energy consumption with a greater magnitude than in the short-run. In the long-run, an increase of 1% in economic growth leads to an increase of 4.6% and 1.1% in energy demand in agriculture and industrial sectors, respectively. Exports are the second largest contributor to energy demand in the overall economy and the agriculture sector. Finally, the technological innovation that enhances energy efficiency is only effective in reducing energy consumption in the industrial sector, which ultimately reduces emissions.

Abstract: Drawing on evidence from a mixed methods retrofit intervention trial of the homes of low-income, older and frail people in Victoria, Australia, this study explored practices of heating and keeping warm in terms of equity and health. In most homes, heating restrictions led to inadequate indoor temperatures. Adaptation practices increased householder resilience, however, some technical responses presented safety risks. Low-cost retrofits did not eliminate underheating and had little effect on householder practices. The study highlights that a promotion of no-cost energy saving activities acknowledges the adaptive capacity of individuals. However, failure to address material and technical conditions and the vulnerability of older people may lead to unintended health risks.

Ethiopia is currently one of the fastest growing economies in Africa. Due to this rapid growth in economy the country is facing a huge challenge to meet the fast growing energy demand. The major demand comes from industrial, agricultural, service sectors and from the rising household consumption because of the rising standard of living. From among the many other sources of energy, the country identified hydropower to be the key to satisfy the current growing energy demand. When the government decided to develop huge hydropower projects on the country’s major river basins, it not only considered the country’s huge hydropower potential but also the additional social, economic and environmental benefits these multi-purpose hydropower projects bring. These hydropower projects also provide the opportunity to mitigate their minimal negative environmental impacts. This paper will explore the country’s hydropower potential, energy consumption, and future energy demand. Then it discusses the role of hydropower in terms of satisfying the energy demand and the advantages it provides as compared to other alternative energy technologies. Finally, the part hydropower plays in leading the country into a more sustainable energy future is explored as well.

Abstract Artificial Intelligence (AI) is becoming the engine of a new round of technological revolution and industrial transformation; as such, it has attracted much attention of scholars in recent years. Surprisingly, scarce studies have shed lights on the effects of AI on the environment, especially with respect to carbon intensity. Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, we use Chinese industrial sector data from 2005 to 2016 to investigate how AI affects carbon intensity. The empirical results show that AI, as measured separately by the adoption of robotics by industry and the number of academic AI-related papers, significantly reduces carbon intensity. The results remain robust after addressing endogenous issues. We find that there are both stages and industrial heterogeneity in the effects of AI on carbon intensity. AI had a more decrease effect on carbon intensity during the 12th Five-Year Plan than the 11th. Compared with capital-intensive industries, AI tends to have a more decrease effect on carbon intensity in the labor-intensive and tech-intensive industries. To enlarge the effects of AI on reducing carbon intensity, the government should promote the development and application of AI and implement differentiated policies in line with the industry characteristics.