• Energy Research
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There is a growing awareness of the serious problems associated with the provision of sufficient energy to meet human needs and to fuel economic growth world-wide. This has pointed to the need for energy and material efficiency, which would reduce air, water and thermal pollution, as well as waste production. Increasing energy and material efficiency also have the benefits of increased employment, improved balance of imports and exports, increased security of energy supply, and adopting environmentally advantageous energy supply. A large potential exists for energy savings through energy and material efficiency improvements. Technologies are not now, nor will they be, in the foreseeable future, the limiting factors with regard to continuing energy efficiency improvements. There are serious barriers to energy efficiency improvement, including unwillingness to invest, lack of available and accessible information, economic disencentives and organizational barriers. A wide range of policy instruments, as well as innovative approaches have been tried in some countries in order to achieve the desired energy efficiency approaches. These include: regulation and guidelines; economic instruments and incentives; voluntary agreements and actions, information, education and training; and research, development and demonstration. An area that requires particular attention is that of improved international co-operation to develop policy instruments and technologies to meet the needs of developing countries. Material efficiency has not received the attention that it deserves.Consequently, there is a dearth of data on the qualities and quantities for final consumption, thus, making it difficult to formulate policies. Available data, however, suggest that there is a large potential for improved use of many materials in industrialized countries.

China, with its growing population and economic development, faces increasing risks to health from climate change, but also opportunities to address these risks and protect health for generations to come. Without a timely and adequate response, climate change will impact lives and livelihoods at an accelerated rate. In 2020, the Lancet Countdown Regional Centre in Asia, led by Tsinghua University, built on the work of the global Lancet Countdown and began its assessment of the health profile of climate change in China with the aim of triggering rapid and health-responsive actions. This 2021 report is the first annual update, presenting 25 indicators within five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. The report represents the contributions of 88 experts from 25 leading institutions in, and outside of, China. From 2020 to 2021, five new indicators have been added and methods have been improved for many indicators. Where possible, the indicator results are presented at national and provincial levels to facilitate local understanding and policy making. In a year marked by COVID-19, this report also endeavours to reflect on China's pathway for a green recovery, ensuring it aligns with the carbon neutrality goal, for the health of the current and future generations.

Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

Abstract ENERGY STAR is a voluntary energy efficiency-labeling program operated jointly by the United States Department of Energy and the United States Environmental Protection Agency (US EPA). Since the program's inception in 1992, ENERGY STAR has become a leading international brand for energy-efficient products. ENERGY STAR's central role in the development of regional, national, and international energy programs necessitates an open process whereby its program achievements to date as well as projected future savings are shared with committed stakeholders. Through 2006, US EPA’S ENERGY STAR labeled products saved 4.8 EJ of primary energy and avoided 82 Tg C equivalent. We project that US EPA’S ENERGY STAR labeled products will save 12.8 EJ and avoid 203 Tg C equivalent over the period 2007–2015. A sensitivity analysis examining two key inputs (carbon factor and ENERGY STAR unit sales) bounds the best estimate of carbon avoided between 54 and 107 Tg C (1993–2006) and between 132 and 278 Tg C (2007–2015).

AbstractTwenty-five years since foundational publications on valuing ecosystem services for human well-being1,2, addressing the global biodiversity crisis3 still implies confronting barriers to incorporating nature’s diverse values into decision-making. These barriers include powerful interests supported by current norms and legal rules such as property rights, which determine whose values and which values of nature are acted on. A better understanding of how and why nature is (under)valued is more urgent than ever4. Notwithstanding agreements to incorporate nature’s values into actions, including the Kunming-Montreal Global Biodiversity Framework (GBF)5 and the UN Sustainable Development Goals6, predominant environmental and development policies still prioritize a subset of values, particularly those linked to markets, and ignore other ways people relate to and benefit from nature7. Arguably, a ‘values crisis’ underpins the intertwined crises of biodiversity loss and climate change8, pandemic emergence9 and socio-environmental injustices10. On the basis of more than 50,000 scientific publications, policy documents and Indigenous and local knowledge sources, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) assessed knowledge on nature’s diverse values and valuation methods to gain insights into their role in policymaking and fuller integration into decisions7,11. Applying this evidence, combinations of values-centred approaches are proposed to improve valuation and address barriers to uptake, ultimately leveraging transformative changes towards more just (that is, fair treatment of people and nature, including inter- and intragenerational equity) and sustainable futures.

In 2005, a comprehensive comparison of life cycle impact assessment toxicity characterisation models was initiated by the United Nations Environment Program (UNEP)–Society for Environmental Toxicology and Chemistry (SETAC) Life Cycle Initiative, directly involving the model developers of CalTOX, IMPACT 2002, USES-LCA, BETR, EDIP, WATSON and EcoSense. In this paper, we describe this model comparison process and its results—in particular the scientific consensus model developed by the model developers. The main objectives of this effort were (1) to identify specific sources of differences between the models’ results and structure, (2) to detect the indispensable model components and (3) to build a scientific consensus model from them, representing recommended practice. A chemical test set of 45 organics covering a wide range of property combinations was selected for this purpose. All models used this set. In three workshops, the model comparison participants identified key fate, exposure and effect issues via comparison of the final characterisation factors and selected intermediate outputs for fate, human exposure and toxic effects for the test set applied to all models. Through this process, we were able to reduce inter-model variation from an initial range of up to 13 orders of magnitude down to no more than two orders of magnitude for any substance. This led to the development of USEtox, a scientific consensus model that contains only the most influential model elements. These were, for example, process formulations accounting for intermittent rain, defining a closed or open system environment or nesting an urban box in a continental box. The precision of the new characterisation factors (CFs) is within a factor of 100–1,000 for human health and 10–100 for freshwater ecotoxicity of all other models compared to 12 orders of magnitude variation between the CFs of each model, respectively. The achieved reduction of inter-model variability by up to 11 orders of magnitude is a significant improvement. USEtox provides a parsimonious and transparent tool for human health and ecosystem CF estimates. Based on a referenced database, it has now been used to calculate CFs for several thousand substances and forms the basis of the recommendations from UNEP-SETAC’s Life Cycle Initiative regarding characterisation of toxic impacts in life cycle assessment. We provide both recommended and interim (not recommended and to be used with caution) characterisation factors for human health and freshwater ecotoxicity impacts. After a process of consensus building among stakeholders on a broad scale as well as several improvements regarding a wider and easier applicability of the model, USEtox will become available to practitioners for the calculation of further CFs.