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Abstract Coal energy landscapes have changed dramatically over the last decades, including geographic shifts in production and consumption, technological changes that have reduced labour demand and led to relatively new mining practices (e.g. invasive mountain-top approaches), changed economic footprints, a shutdown of capacities or a complete end of mining in many regions with massive impacts on regional and local economies, community well-being, social capital, et cetera. Then the Covid-19 pandemic and Russia´s invasion of Ukraine have fundamentally affected the global economy, disrupted energy markets, and shattered existing estimates about development trends, challenging the progress and speed of the low-carbon energy transition and coal phase-out. This article provides a brief reflection on the changing landscapes of coal and their possible futures, and serves as an introduction to the Special Issue of Moravian Geographical Reports on “The death of coal in the energy transition? Regional perspectives”.

A focus on land-use and forests as a means to reduce carbon dioxide levels in the global atmosphere has been at the heart of the international climate change debate since the United Nations Kyoto Protocol was agreed in 1997. This environmental management practice is a process technically referred to as mitigation. These largely technical projects have aimed to provide sustainable development benefits to forest-dependent people, as well as to reduce greenhouse gas emissions. However, these projects have had limited success in achieving these local development objectives. This article argues that this is due in part to the patriarchal underpinnings of the sustainable development and climate-change policy agendas. The author explores this theory by considering how a climate mitigation project in Bolivia has resulted in different outcomes for women and men, and makes links between the global decision-making process and local effects.

China and India are the world's largest developing economies and also two of the most populous countries. China, which now has more than 1.3 billion people, is expected to grow to more than 1.4 billion by 2050, and India with a population of I billion will overtake China to be the most populous country with about 1.6 billion population. These two countries are home to 37% of the world's population today. In addition, China and India have achieved notable success in their economic development characterised by a high rate of gross domestic product (GDP) growth in the last two decades. Together the two countries account already for almost a fifth of world GDP. The most direct and significant result of economic growth in India and China is the amazing improvement in quality of life (or at least spending power) for an increasing share of the population. The populations of both the countries have experienced a transition from 'poverty' to 'adequate food and clothing',- today growing parts of the population are getting closer to 'well to do lifestyles'. These seLyments of the society are not satisfied any more with enough food and clothes, but are also eager to obtain a quality life of high nutrient food, comfortable living, health care and other quality services. The theme of this paper is to analyse how the major drivers contributed to the environmental consequences in the past, and to take a forward look at the environmental impacts of these driving forces in China and India. The paper identifies population, affluence and technology to be the major drivina forces in environmental pollution for these two countries then applies the simple equation of Impact = Population x Affluence x Technology, or I = PAT to evaluate the effects of changes in these drivers on CO, emissions. (C) 2007 Elsevier Ltd. All rights reserved.

Recent theoretical literature in social policy argued that climate change posed a new risk to the states and called for transformation from a traditional welfare state to an ‘eco’ state. From a theoretical point of view, different welfare regimes may manage environmental/climate change risks in a similar way to social risks. However, not much has been done to explore the issue empirically. To this end, this paper aims to investigate public attitudes towards environmental and traditional welfare policies given that environmental change is a new social risk the welfare states have to address. Do individuals that care for one area also care for the other? That is, do the preferences in these two policy spheres complement or substitute one another? We test these hypotheses both at the individual- and country-level, using data from 14 countries included in all three waves (1993, 2000, and 2010) of the environmental module in the International Social Survey Programme. Specifically, we investigate the relationship between attitudes towards income redistribution (indicator of support for welfare policy) and willingness to pay for environmental protection (indicator of support for environmental policy). Our findings suggest that attitudes in the two areas are substitutes in the total sample, but that the relationship is very small and only statistically significant in some specifications. When we explore country differentials, we observe clear heterogeneity in the relationship, which can be explained by differences in political and historical contexts across countries.

Abstract Cities are the major contributors to energy consumption and CO2 emissions, as well as being leading innovators and implementers of policy measures in climate change mitigation. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is an agglomeration of cities put forward by China to strengthen international cooperation among “Belt and Road” countries and promote low-carbon, inclusive, coordinated and sustainable development. Few studies have discussed the emission characteristics of GBA cities. This study, for the first time, compiles emission inventories of 11 GBA cities and their surroundings based on IPCC territorial emission accounting approach, which are consistent and comparable with the national and provincial inventories. Results show that (a) total emissions increased from 426 Mt in 2000 to 610 Mt in 2016, while emissions of GBA cities increased rapidly by 6.9% over 2000–2011 and peaked in 2014 (334 Mt); (b) raw coal and diesel oil are the top two emitters by energy type, while energy production sector and tertiary industry are the top two largest sectors; (c) GBA cities take the lead in low-carbon development, emitted 4% of total national emissions and contributed 13% of national GDP with less than a third of national emission intensities and less than three-quarters of national per capita emissions; (d) Macao, Shenzhen and Hong Kong have the top three lowest emission intensity in the country; (e) most of GBA cities are experiencing the shift from an industrial economy to a service economy, while Hong Kong, Shenzhen, Foshan and Huizhou reached their peak emissions and Guangzhou, Dongguan and Jiangmen remained decreasing emission tendencies; (g) for those coal-dominate or energy-production cities (i.e. Zhuhai, Zhongshan, Zhaoqing, Maoming, Yangjiang, Shanwei, Shaoguan and Zhanjiang) in mid-term industrialization, total emissions experienced soaring increases. The emission inventories provide robust, self-consistent, transparent and comparable data support for identifying spatial–temporal emission characteristics, developing low-carbon policies, monitoring mitigation progress in GBA cities as well as further emissions-related studies at a city-level. The low-carbon roadmaps designed for GBA cities and their surroundings also provide a benchmark for other developing countries/cities to adapting changing climate and achieve sustainable development.

This paper uses data from Understanding Society: the UK Household Longitudinal Study to explore the association between fuel poverty and a set of wellbeing outcomes: life-satisfaction, self-reported health measures and more objectively measured biomarker data. Over and above the conventional income–fuel cost indicators, we also use more proximal heating deprivation indicators. We create and draw upon a set of composite indicators that concomitantly capture (the lack of) affordability and thermal comfort. Depending on which fuel deprivation indicator is used, we find heterogeneous associations between fuel poverty and our wellbeing outcomes. Employing combined fuel deprivation indicators, which takes into account the income–fuel cost balance and more proximal perceptions of heating adequacy, reveals the presence of more pronounced associations with life satisfaction and fibrinogen, one of our biological health measures. The presence of these strong associations would have been less pronounced or masked when using separately each of the components of our composite fuel deprivation indicators as well as in the case of self-reported generic measures of physical health. Lifestyle and chronic health conditions play a limited role in attenuating our results, while material deprivation partially, but not fully, attenuates our associations between fuel deprivation and wellbeing. These results remain robust when bounding analysis, IV and panel data models are employed to test the potential role of various sources of endogeneity biases. Our analysis suggests that composite fuel deprivation indicators may be useful energy policy instruments for uncovering the underlining mechanism via which fuel poverty may get “under the skin”.

There are substantial differences in carbon footprints across households. This study applied an environmentally extended multiregional input–output approach to estimate household carbon footprints for 12 different income groups of China’s 30 regions. Subsequently, carbon footprint Gini coefficients were calculated to measure carbon inequality for households across provinces. We found that the top 5% of income earners were responsible for 17% of the national household carbon footprint in 2012, while the bottom half of income earners caused only 25%. Carbon inequality declined with economic growth in China across space and time in two ways: first, carbon footprints showed greater convergence in the wealthier coastal regions than in the poorer inland regions; second, China’s national carbon footprint Gini coefficients declined from 0.44 in 2007 to 0.37 in 2012. We argue that economic growth not only increases income levels but also contributes to an overall reduction in carbon inequality in China.

The Bono Social de Electricidad (BSE) is a government programme, introduced in 2009, to reduce energy poverty in Spain. The BSE is a discount on the price of electricity, available to vulnerable households who applied. Applying differences-in-differences and propensity score matching to household data between 2008 and 2011, we find no statistically significant impact of the intention to treat on two indicators of energy poverty, viz. the ability to keep the house adequately warm, and the presence of damp walls, rotting windows and leaking roofs. This may be because eligible households did not apply. A third indicator, delays in paying electricity bills, showed a statistically significant deterioration. That is, the BSE has not reduced energy poverty, if anything it has made it worse. This is not because eligible households transferred income to relatives hit harder by the financial crises, but it may be because the BSE discount did not fully compensate for the cold of 2010.