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Abstract The consumption of takeaway food is increasing worldwide. Single-use containers used for takeaway food represent a significant source of waste and environmental impacts due to their low recyclability. Consequently, it is important to identify the best available alternatives and improvement opportunities to reduce the environmental impacts of fast-food containers. For these purposes, this study estimates and compares for the first time the life cycle impacts of three most widely-used types of takeaway container: aluminium, polypropylene and extruded polystyrene. These are also compared to reusable polypropylene containers. The findings suggest that single-use polypropylene containers are the worst option for seven out of 12 impacts considered, including global warming potential. They are followed by the aluminium alternative with five highest impacts, including depletion of ozone layer and human toxicity. Overall, extruded polystyrene containers have the lowest impacts due to the lower material and electricity requirements in their manufacture. They are also the best option when compared to reused takeaway polypropylene containers, unless the latter are reused 3–39 times. The number of uses needed for the reusable “Tupperware” polypropylene food savers is even higher, ranging from 16 to 208 times, with terrestrial ecotoxicity being always higher than for extruded polystyrene, regardless of the number of uses. However, extruded polystyrene containers are currently not recycled and cannot be considered a sustainable option. If they were recycled in accordance with the European Union 2025 policy on waste packaging, most of their impacts would be reduced by >18%, while also reducing littering and negative effects on marine organisms. Most of the impacts of the other two types of container would also be reduced (>20%) through increased recycling. Implementing the European Union 2025 policy on recycling of waste packaging would reduce all the impacts by 2%–60%, including a 33% reduction in global warming potential. Based on 2025 million takeaway containers used annually in the European Union, the latter would save 61,700 t CO2 eq./yr, equivalent to the emissions of 55,000 light-duty vehicles. The outcomes of this study will be of interest to packaging manufacturers, food outlets, policy makers and consumers.

Abstract The Chinese government has taken measures to realize energy-savings and emission reductions, such as promoting innovations, adjusting the industrial structure, balancing regional development, and reforming markets. The aim of this paper is to assess the effects of these measures on China's CO2 emissions by using a newly proposed decomposition approach, which identified eight new factors related to the above realistic measures, i.e., energy saving and production technologies, industrial energy and production efficiencies, regional energy and production efficiencies, and pure energy and production efficiencies. The main findings indicate benefits from considerable technological progress in energy-saving and production during 2000–2016 period, and two technological factors contributed the most to emissions abatement and cumulatively reduced 5372.43 Mt and 1291.72 Mt CO2 emissions. The efforts of industrial restructuring promoted energy and production efficiency improvement, which further facilitated emission reduction. In contrast, the pure energy and production efficiency changes cumulatively led to 1080.26 Mt and 1135.85 Mt CO2 emissions growth during the whole sample period, suggesting that severe resource misallocation problems may exist in both the energy market and output market. Additionally, the Chinese government failed to narrow the technology gap between developed regions and underdeveloped regions, further restricting emission reduction.

Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

The COVID-19 pandemic has tremendously affected the whole of human society worldwide. Travel patterns have greatly changed due to the increased risk perception and the governmental interventions regarding COVID-19. This study aimed to identify contributing factors to the changes in public and private transportation mode choice behavior in China after COVID-19 based on an online questionnaire survey. In the survey, travel behaviors in three periods were studied: before the outbreak (before 27 December 2019), the peak (from 20 January to 17 March 2020), and after the peak (from 18 March to the date of the survey). A series of random-parameter bivariate Probit models was developed to quantify the relationship between individual characteristics and the changes in travel mode choice. The key findings indicated that individual sociodemographic characteristics (e.g., gender, age, ownership, occupation, residence) have significant effects on the changes in mode choice behavior. Other key findings included (1) a higher propensity to use a taxi after the peak compared to urban public transportation (i.e., bus and subway); (2) a significant impact of age on the switch from public transit to private car and two-wheelers; (3) more obvious changes in private car and public transportation modes in more developed cities. The findings from this study are expected to be useful for establishing partial and resilient policies and ensuring sustainable mobility and travel equality in the post-pandemic era.

This dataset currently consists of a single excel file which contains the Scottish Social Accounting Matrix for 2013, with households being disaggregated into quintiles based on their weekly income. The dataset has been used to study the impact of Energy Efficient Scotland programme and associated work that explored how the anticipated impacts may change due to Brexit

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.BCC.BCC-ESM1.piControl' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The BCC-ESM 1 climate model, released in 2017, includes the following components: atmos: BCC_AGCM3_LR (T42; 128 x 64 longitude/latitude; 26 levels; top level 2.19 hPa), atmosChem: BCC-AGCM3-Chem, land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 250 km, atmosChem: 250 km, land: 250 km, ocean: 50 km, seaIce: 50 km.

Project: Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets - These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ). The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. Summary: These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.BCC.BCC-CSM2-HR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The BCC-CSM 2 HR climate model, released in 2017, includes the following components: atmos: BCC_AGCM3_HR (T266; 800 x 400 longitude/latitude; 56 levels; top level 0.1 hPa), land: BCC_AVIM2, ocean: MOM4 (1/3 deg 10S-10N, 1/3-1 deg 10-30 N/S, and 1 deg in high latitudes; 360 x 232 longitude/latitude; 40 levels; top grid cell 0-10 m), seaIce: SIS2. The model was run by the Beijing Climate Center, Beijing 100081, China (BCC) in native nominal resolutions: atmos: 50 km, land: 50 km, ocean: 50 km, seaIce: 50 km.

With China’s urban renewal, parks have developed into significant green recreational areas in cities. This paper analyzed social media texts and compared the evaluation outcomes of the 50 most popular urban parks in Beijing from various perspectives, such as the characteristics of various groups of people, park types, and the spatial and temporal distribution characteristics of recreational activities. The importance–performance analysis method was used to analyze the main factors affecting visitors’ satisfaction with parks. The research found the following: (1) Positive evaluation of parks was related to environmental construction, event organization, etc., and negative evaluations focused on ticket supply, consumer spending, etc. (2) Visitors of different genders and from different regions focused on different aspects of parks. (3) In terms of traffic accessibility, historical and cultural display, parent–child activity organization, and ecological environment experience, people had diverse demands from various types of parks. (4) People were more likely to visit parks located within the range of all green belts in springs and parks located in the second green isolation belt in the fall. (5) The number of non-holiday reviews of parks was higher than that of holiday reviews. (6) Managers could improve visitor satisfaction by improving the infrastructure and management of parks.

Virtual water flows have a profound impact on the natural water system of a country or region, and they may help conserve local water resources or exacerbate water scarcity in some areas. However, current research has only focused on the measurement of virtual water flows, without analysis of the causes of virtual water flow patterns. This study first obtained virtual water flow patterns across provinces by constructing a multi-regional input–-output (MRIO) model of the Yellow River basin in 2012 and 2017, and then analyzed its driving factors by applying the extended STIRPAT model to provide directions for using virtual water trade to alleviate water shortages in water-scarce areas of the basin. We found the following: (1) The Yellow River basin as a whole had a net virtual water inflow in 2012 and 2017, and the net inflow has increased from 2.14 billion m3 to 33.67 billion m3. (2) Different provinces or regions assume different roles in the virtual water trade within the basin. (3) There is an obvious regional heterogeneity in the virtual water flows in different subsectors. (4) Per capita GDP, tertiary industry contribution rate, consumer price index, and water scarcity are the main positive drivers of virtual water inflow in the Yellow River Basin provinces, while primary industry contribution rate, per capita water resources, and water use per unit arable area promote virtual water outflow. The results of this paper present useful information for understanding the driving factors of virtual water flow, which could promote the optimal allocation of water resources in the Yellow River basin and achieve ecological protection and high-quality development in this area.