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The motivation of this study stems from the United Nations Sustainable Development Goals on clean and responsible energy consumption, climate change mitigation and sustainable economic growth (UN-SDGs-7, 11, 12 and 13). The present study examines the impact of institutional quality on CO 2 emissions in the presence of the Environmental Kuznets Curve (EKC) framework using data on CO 2 emissions and the six dimensions of institutional quality from the World Governance Indicators (WGI). The current study focuses on selected 30 Sub-Saharan African (SSA) countries over the annual period from 2000 to 2021. The EKC hypothesis revealed CO 2 emissions are substantially reduced by corruption control, regulatory quality, and the rule of law. Findings from the Dumitrescu and Hurlin causality test showed a one-way causality running from CO 2 emissions to industrialization. Similar uni-directional causality is observed between economic growth, and energy consumption. On the other hand, we observed a two-way causality flow from CO 2 emissions to population growth and all indices of institutional quality over the investigated period. These findings indicate that government agencies should efficiently implement acceptable strategies for pollution control and enact public benefit environmental regulations in the form of a healthier climate for the entire population. • We examined the impact of institutional quality on CO 2 emissions in the presence of the Environmental Kuznets Curve (EKC) framework. • 30 Sub-Saharan Africa (SSA) countries are selected for empirical investigation. • We reveal that CO 2 emissions are substantially reduced by corruption control, regulatory quality, and the rule of law. • Meanwhile, one-way causality runs from CO 2 emissions to industrialization. • A two-way causality flow from CO 2 emissions to population growth and all indices of institutional quality are reported. [ABSTRACT FROM AUTHOR]

The increasing building energy consumption in China has been acknowledged as a key concern in future climate mitigation and sustainable development. Though reliable accounting of energy consumption is the prerequisite, estimates of China's building energy consumption remain subject to large discrepancies. One of the primary causes of these disparities is an inaccurate estimation of building energy intensities due to flaws in estimation methods and data. Here, we present a bottom-up approach and use large-scale nationwide building energy survey data (covering 30 provinces, four different types of buildings, and district heating from 2009 to 2016) to correct the existing, widely used assumption of normal distribution for building energy intensity and provide a more accurate estimate of building energy consumption in China. Our improved estimate of China's building energy consumption differs by −10% to 36% from existing mainstream estimates, indicating a significant bias in existing research and applications. • The non-normal probability distribution of building energy intensity is identified. • A bottom-up approach for the accurate estimate of building energy use is developed. • The re-quantified building energy use differs by −10% ~ 36% from the main estimates. • The features of building energy consumption in China were explored. [ABSTRACT FROM AUTHOR]

• Buildings potential for climate change mitigation is high. • Mitigation actions in the buildings sector can have significant social and economic values. • These enable the achievement of 4 SDGs, reinforce 7 other. • And are linked to the achievement of 5 SDGs (SDG3, SDG7, SDG8, SDG11, and SDG13). • Trade-offs were identified for 9 SDGs. Buildings are responsible for a big part of total GHG emissions; therefore, their climate change mitigation potential is high. But research shows that mitigation actions in the sector can have significant social and economic values beyond reduction of energy consumption and the associated GHG emissions. This paper summarizes these multiple impacts of mitigation actions in buildings, which are related to microeconomic and macroeconomic effects, health impacts, environmental benefits, better management of natural resources, impacts on social well-being, and energy security implications, and assesses their interactions (synergies and trade-offs) with the UN SDGs. The findings of our analysis show that climate change mitigation actions in buildings enable the achievement of 4 SDGs, reinforce 7 others, and are inextricably linked to the achievement of 5 SDGs, namely SDG3: good health and well-being, SDG7: affordable and clean energy, SDG8: decent work and economic growth, SDG11: sustainable cities and communities, and SDG13: climate action. On the other hand, trade-offs were identified for 9 SDGs, with rather minor implications, which can be further eliminated with the implementation of appropriate policies. [ABSTRACT FROM AUTHOR]

This study aims to unveil the heterogeneous impacts of import product diversification and institutional factors for achieving energy efficiency in Organization for Economic Co-operation and Development (OECD) countries. In doing so, the study employs robust econometric techniques such as Fully modified ordinary least squares (FMOLS) co-integration, pooled Ordinary Least Squares (OLS), Feasible Generalized Least Squares (FGLS), pool mean group regressions, fixed effects, and random effects for panel data from 1990 to 2015 for selected OECD countries. The detailed empirical outcomes suggest that import product diversification is conducive to reducing the energy and carbon intensity (improvement in energy efficiency) in OECD countries. The empirical conclusions provide various guidelines to achieve cleaner and greener growth and align with various Sustainable Development Goals (SDG 7: Affordable and clean energy, SDG 9: Industry, innovation, and infrastructure, and SDG 13: Climate action) of OECD countries. The paper elaborates fruitful policy suggestions regarding the diversification of imports and energy use-carbon emission-nexus for the OECD member nations. Based on the findings, policymakers and environmental scientists should strengthen the trade-energy and import portfolio policies to attain energy efficiency. [ABSTRACT FROM AUTHOR]

GEP (Generation Expansion Planning) is one of the most complex tasks in which planning is done to find the best possible distinct solutions to install new generating units to meet future load demands. This paper uses the Low Emission Analysis Platform (LEAP) software environment to analyse the GEP for the AP Power sector from 2021 to 2050. In recent years, the AP power sector has faced several issues, such as frequent service cuts due to power shortages. In this paper, five scenarios such as Business As Usual (BAU), Low Demand Growth (LDG), High Demand Growth (HDG), Least Cost Optimization (LCO) and Climate Mitigation Scenarios, have been analysed with environmental, technical, and economic parameters. This study has estimated the total environmental emissions and total cost of all scenarios. At present, Andhra Pradesh has a total installed capacity of 25.4 GW. The best scenarios can be selected based on environmental impact, cost-effectiveness, efficiency, and reliability. The results show that the LDG scenario will be an optimal choice for AP due to the state's economic crisis, and the government should also focus on energy conservation and limit demand growth to 4.5%. While presenting a general GEP, the study presents comprehensive system performance scenarios from which planners can choose. • Making a generation expansion planning • Making a sustainable environment • Presented a good solution for power generation • Analysed the total environmental emissions • Analysed the energy demands. [ABSTRACT FROM AUTHOR]

Climate change adaptation and mitigation remain critical to achieving sustainable development while reducing climate vulnerability, particularly among climate-exposed and sensitive regions. Yet, achieving a balance between climate-resilience pathways, high economic productivity, high human development, and energy efficiency appears complex, leading to potential trade-offs. Here, we examine the overarching effect of the diversified energy portfolio, socio-economic drivers, and governance adaptation readiness on Climate change vulnerability across 212 economies. Contrary to the poor conventional panel techniques reported in the existing literature, we employ novel machine learning and dynamic panel estimation techniques that control for chaos, nonlinearity, mutual coupling, and heterogeneity in dynamic systems. The convergent cross-mapping causality technique reveals mutual coupling effects between energy portfolio, governance readiness, socio-economic drivers, and climate change vulnerability. The rapidly increasing population and increasing demand for resources under the business-as-usual society and economic structure that normalizes unsustainable development pathways due to weak governance structures create ineffective climate-resilient policies that lead to unabated emissions with consequences on climate change. The effect of social and governance readiness leads the transformation process to attain sustainable development. Thus, high social and governance readiness spurs climate resilience through climate change adaptation and mitigation to achieve sustainable development. Alternative (renewables) and nuclear energy have displacement effects on fossil fuels, yet, the magnitude of displacement is not large enough to replace future fossil fuel consumption. Conversely, a low-carbon future is still attainable by replacing the fossil energy portfolio with more natural gas and carbon-abatement technologies. Our study demonstrates that energy innovations are useful climate-resilience pathways that lessen climate change vulnerability. [ABSTRACT FROM AUTHOR]

Climate change mitigation has been a global effort and energy generation is one of the primary sources of GHG emissions. There is a need to transition from fossil-based energy to renewable-based energy. One of the platforms to nourish the work is Eco-Industrial Park (EIP) which it promotes resource efficiency, industrial symbiosis, and circular economy practice. There is a need to explore an optimal configuration of multiple renewable energy (RE) systems in a centralised EIP facility that generates electricity for internal consumption and exports the excess energy to the grid. This model developed a mixed-integer non-linear programming (MINLP) model that considers RE from biogas, biomass, micro-hydro, and solar sources for a centralised water hub. The energy generated is primarily used for the main processes involved in the facility as the excess energy will be supplied to the grid. A case study and two scenarios were assessed, and the results offered simultaneous technical, economic, and environmental benefits, which are in the forms of multiple RE generations, additional yearly profit, and GHG emission avoidance from fossil source. This will further strengthen the envisioned role of EIP as it is also capable of supporting the United Nations Sustainable Development Goal (UNSDG) and RE100 agenda. • A MINLP model to optimise renewable energy generation for Total Site was developed. • The model is a part of a framework to optimise energy, water, and material streams. • The model considers energy generation via solar, micro hydro, biomass, and biogas. • The proposed installation could suffice the core processes energy needs. • Additional profit is up to 39% and GHG emission reduction can be up to 70%. [ABSTRACT FROM AUTHOR]

Reducing CO 2 emissions progressively moves up on national and international political agendas. Hopes for "carbon neutrality" heavily rely on renewable energies, especially wind and solar. However, actions against climate change are not at zero cost and can exert large negative environmental and social impacts. The land footprints of wind, solar and hydropower are dozens to hundreds of times larger than other alternatives to fossil fuels, such as nuclear power. However, evidence-based calls to seriously consider nuclear power as part of the energy mix needed to get rid of fossil fuels are systematically ignored, as nuclear energy has been progressively ruled out from the energy transition debate, particularly in most advanced economies. Since first reports on the topic, habitat degradation still ranks as the largest driver of biodiversity loss (e.g. a 68% decrease in vertebrate populations since 1970), contributing between 4 and 14 times more than climate change. Climate action is a Sustainable Development Goal that must be addressed without compromising other Agenda 2030 targets, such as preserving biodiversity. Halting habitat degradation while fighting against climate change will require to seriously re-consider the inclusion of nuclear power into the energy mix for a truly sustainable development, particularly considering that the global energy demand has no signs of slowing down. Otherwise, we risk to degrade what is left of our already decimated nature in the name of climate change. [ABSTRACT FROM AUTHOR]

[Display omitted] • A bi-level joint-probabilistic programming approach is developed. • It can handle leader–follower issues and joint-probabilistic constraints. • The proposed approach is applied to China's multi-regional energy system model. • The optimal schemes during 2021–2050 under three mitigation scenarios are obtained. • Results are helpful to achieve low-carbon goals in multi-regional energy system. Climate change mitigation and renewable resources utilization are becoming particularly urgent for energy system management. In this study, a bi-level joint-probabilistic programming (BJPP) method is developed for planning multi-regional energy system under different mitigation policies and uncertainties. BJPP can handle leader–follower issues in decision-making process as well as examine the risk of violating joint-probabilistic constraints. Based on the BJPP method, a China's multi-regional energy system (named as BJPP_CMES) model is formulated to provide optimal scheme for energy system planning of China over a long-term horizon (2021–2050) by synergistically minimizing carbon dioxide (CO 2) emission and system cost. A series of scenarios associated with different carbon capture and storage (CCS) levels and violation risks of energy-demand constraints are examined. Results reveal that: (i) the share of non-fossil energy in China's energy supply would keep increasing in 2021–2050, and the highest growth of the renewable supply would occur in Ningxia (rising 47.7%); (ii) Sichuan, Inner Mongolia, and Gansu would be the top three suppliers of renewable electricity; (iii) the CO 2 emission of China would reach a peak of [44.3, 54.8] billion tonnes during the period of 2026–2030; Shandong, Inner Mongolia, and Shanxi would be main contributors of CO 2 emission in the future; (iv) compared with the single-level model, the CO 2 emission from the BJPP_CMES model would reduce by [2.7, 5.7]%; (v) among developed regions, the individual probability level of Jiangsu-Zhejiang-Shanghai is the most significant parameter for both CO 2 emission and system cost. The findings are helpful for decision makers to optimize multi-regional energy system (MES) with a low-carbon and cost-effective manner, as well as to provide useful information for renewable energy utilization and regional sustainable development. [ABSTRACT FROM AUTHOR]

• GDP growth is the main contributor to GHG emissions increase in low-income countries. • Decreases in energy intensity had the highest impact on GHG emission reductions. • Greater RES and EE efforts are necessary to offset effects from growing economy. • Current climate policies in the Baltics are not sufficient to achieve 2030 targets. The global economy is on the verge of one of the greatest transitions in modern history. The ability to ensure sustainable economic development and prosperity while significantly reducing consumption of energy resources and generated greenhouse gas emissions is a global challenge that affects every country in the world. To assess whether economies are ready for this challenge, there is an urgent need to examine this dual relationship between economic growth and climate change measures. European Green Deal strategy has set the ambitious goal of Europe becoming the first climate-neutral continent by 2050, boosting competitiveness and long-term prosperity of the economy. Kaya identity and LMDI decomposition is applied to examine how European Union countries have been coping with these countereffects historically. The decomposition analysis is conducted for the EU-28 (including the UK) countries for a 10-year study period from 2010 to 2019. This study analyses the main drivers of changes in GHG emissions in European Union and estimates the progress made in implementing the Green Deal targets. The results show that in the EU, energy efficiency improvements have twice the effect on reducing GHG emission compared to RES strategies. The effect of economic growth was the main offsetting factor hindering the achievement of larger GHG emission reductions. More in-depth ex-ante and ex-post investigation is performed for the Baltic States. A novel forecasting technique is applied to project GHG emissions under three different development scenarios, such as the scenario with existing measures, the scenario with additional measures, and the business-as-usual scenario. The results show that the current climate policies in the Baltic States are not sufficient to achieve the 2030 emission reduction targets and that greater efforts should be made to enforce climate mitigation measures in the economies. [ABSTRACT FROM AUTHOR]