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Carbon emissions elimination in the rapid urbanization areas contributes to regional sustainable development and global climate change mitigation. With the target of carbon neutrality, the global top-class Beijing-Tianjin-Hebei Urban Agglomeration which confronts with the dual pressure of excess carbon emissions and shrink ecological space has increased its carbon reduction coordination cooperation, among which energy consumption carbon emission control and natural resource sink maintenance have been recognized as the key strategy. However, the study on carbon balance prediction is lack and the amount of energy consumption carbon emissions in the future can be offset by the ecological carbon sink is still unknown. To estimate the gap to carbon neutrality, this study proposes a framework for carbon offset analysis and prediction which takes various carbon emission scenarios and the potential of natural resource on carbon reduction into consideration. After the framework conduction in the agglomeration, it is revealed that the regional least carbon emissions of 29.51 Mt mainly derive from the population and economic growth brought by urbanization process, while the carbon sink predicted to be 2552.31 Kt is still far to cover the emissions. With the regional carbon offset rate decline from 0.68% to 0.63%, the ecological carbon sink in Beijing will offset 1.21% of its carbon emission, while the least ecological space keep city Tianjin is predicted to suffer the lowest offset rate of 0.09%. The research results contribute to recognize the key factors on carbon emission control and key areas for carbon maintenance in following years, as well as narrowing the gap to neutrality with both consideration of carbon emission reduction and sink maintenance. [Display omitted] • Framework on energy consumption carbon offset analysis was established. • Regional carbon emissions, sinks and offset rate were predicted. • Drop from 0.68% in 2020 to 0.63% in 2030, the declining offset rate indicates the gap to neutrality is still large. • Implications for each area's carbon emission offset rate improvement were discussed. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP , RMSEP , and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives. • An innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting. • The optimum algorithm matching the novel model is determined from five alternatives. • The Monte Carlo Simulation and parameter sensitivity analysis advance the robustness. • PSO with the optimal parameter group has been selected for searching coefficients. • The new model possesses higher accuracy and robustness than various competitors. [ABSTRACT FROM AUTHOR]

Renewable energy consumption is a crucial aspect of global sustainability efforts, particularly in light of climate change mitigation and the pursuit of energy security. Technological innovation can play a pivotal role in driving the advancement and adoption of renewable energy. However, geopolitical risks can exert profound influences on the landscape of renewable energy deployment, posing challenges to energy security and the transition to a sustainable energy future. This study contributes to the existing literature by providing new empirical evidence on how geopolitical risk, along with technological innovation, environmental taxes, and environmental degradation affect renewable energy consumption in G7 countries. The findings indicate that geopolitical risks exert a negative influence on renewable energy consumption in G7 countries, whereas environmental degradation, technological advancements, and environmental taxes have positive effects. The results further indicate that at different quantile levels, the impact of each variable on renewable energy consumption has heterogeneous characteristics. Panel Grager causality results revealed a one-way causal relationship between environmental taxes and renewable energy consumption, while other variables have a two-way causal relationship with renewable energy consumption. Based on the results, the study suggests design policies to address geopolitical tension, increased funding for research and development to drive technological innovation, and supportive regulatory frameworks that incentivize renewable energy investment and innovation to achieve Sustainable Development Goal 7 (SDG7). • Explored the determinants of renewable energy consumption in G7 countries. • Advanced panel data estimation methods are employed. • Geopolitical risk negatively affects renewable energy consumption. • Technological innovation and taxes promote renewable energy consumption. • Designed a policy framework to achieve Sustainable Development Goal 7. [ABSTRACT FROM AUTHOR]

Energy efficiency measures are a key element in the fight against climate change. However, their effectiveness is threatened by so-called rebound effects—behavioral changes that lead to increased use and thus to lower-than-expected absolute savings. The moral licensing bias might cause rebound effects on the individual level: When people implement energy efficiency measures, they may feel that they have done something good for the climate and thus license themselves to less climate-friendly behavior later on. We test this prediction with three pre-registered experiments (total n = 2315). While our manipulated efficiency does increase the feeling of having done something good for the climate, no negative effects emerge on subsequent climate-friendly behavior—contrary to the predictions derived from the moral licensing literature. Climate-friendly behavior is thus not hampered by prior energy efficiency behavior; rather, demographics (women and older people act in a more climate-friendly manner) and environmental attitude determine it. Our results imply that the literature suggesting moral licensing is probably not transferable to the field of energy efficiency, and thus moral licensing may not lead to rebound effects in this context. We discuss possible explanations, implications for climate policy making, and needs for future research. [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]

Future projections indicate an expansion for both food and energy demands, which can increase pressure on land use, while there is an urgent global need for climate change mitigation. Bioenergy is foreseen as key option to meet future energy demands and reduce greenhouse gas (GHG) emissions, however its sustainability depends on locations and regional characteristics. Simultaneously, agricultural production models that co-produce food, feed, and energy offer sustainability synergies and co-benefits, but site-specific sustainability assessments of their large-scale implementation are missing. This study presents a bottom-up approach to assess spatially explicit sustainability aspects of bioenergy-livestock integrated systems (BLI) in Brazil and shed light on their contribution to future energy demands, to climate change mitigation targets, and their impacts on selected ecosystem services, including bioenergy production, climate change mitigation, reduction of food competition, biodiversity conservation, and avoided deforestation. The proposed integration considers livestock intensification and use of biofuels by-products as animal feed supplement, taking advantage of synergies between these two value chains. The expansion of the BLI system in the Center-South region of Brazil produce up to 89 billion liters of ethanol, enough to meet future domestic ethanol demands from multiple shared socioeconomic pathways and generating surpluses for export. This production takes place on 16 million hectares of pastureland within the Sugarcane Agroecological Zoning, excluding the Amazon and Pantanal biomes, and biodiversity hotspots. BLI expansion mitigate up to 250 million tonnes of CO 2 eq and generate 15 billion dollars of profits. The best locations to maximize the selected ecosystem services are west of São Paulo, east of Mato Grosso do Sul, and the south-central region of the states of Goiás. This study may encourage the formulation of enhanced public policies for the integration of bioenergy and livestock value chains and guide a sustainable large-scale deployment of BLI systems. • Bioenergy and livestock integration (BLI): co-production of food, feed and energy. • BLI systems: 89 billion liters of ethanol and 250 million tonnes of CO 2 eq mitigated. • Enough ethanol to meet future Brazilian demands and surplus to be exported. • Biomass expansion only on pastureland inside Sugarcane Agroecological Zoning. • Without land displacement and deforestation of native vegetation. [ABSTRACT FROM AUTHOR]

• Chemical process intensification can improve the energy efficiency of chemical production. • The intensification of chemical processes reduces the consumption of raw materials and energy. • Chemical process intensification can help reduce the amount of waste and emissions. • The intensification of chemical processes can encourage innovation and the development of cleaner technologies. • Chemical process intensification can improve safety and health in chemical production. The 2030 Agenda is a call to global action to eradicate poverty, protect the planet and ensure prosperity for all. Process intensification, in this context, refers to improving the efficiency and effectiveness of production and service processes, in order to reduce environmental impact and improve people's quality of life. By optimizing and consolidating various steps within a production process, process intensification aims to achieve higher yields, reduced waste, and lower resource consumption. This approach aligns with the sustainability goals set by the United Nations, including: energy efficiency, waste reduction, resource conservation, emission reduction, circular Economy and sustainable development. Process intensification primarily impacts Goals 7, 9, 12, and 13 of the United Nations' 2023 Sustainability Agenda due to its potential to address key aspects related to energy, industry, sustainable consumption and production, and climate action. This review shows how process intensification directly impacts those Goals of the 2023 Sustainability Agenda by promoting affordable and clean energy, fostering innovation in industry, encouraging responsible consumption and production. [Display omitted] [ABSTRACT FROM AUTHOR]

• Feasibility of net zero-energy (nZE) for residential and commercial building stock. • Accommodation of nZE conditions by current power distribution network. • Proposed framework integrated synthetic population and activities. • Case study on dense urban area in noncold climate. • nZE transformation pathways explored as different from those for cold climate. The transformation to net zero-energy (nZE) building stocks involves harvesting of renewable energy, enhancement of building energy efficiency, incorporation of various supply-side options, and advanced energy management. Although several studies have evaluated the feasibility of nZE for residential areas in cold regions, only a few studies have been conducted on dense urban regions in noncold climate. Additionally, the accommodation of such building stock transformation by the current power distribution network has not been clarified to date. Thus, this study proposes a novel framework integrating synthetic population, activity, building stock, and power distribution network to explore transformation pathways. This was further demonstrated by a case study on a densely populated region covering four cities in Tokyo, Japan. The results signified that complete dissemination of popular energy efficiency measures can reduce the energy demand by 40%. With the reduction in energy demand, nearly nZE condition can be realized only if the building roof area is completely covered by photovoltaics. In particular, one half of the generation would be utilized locally, whereas the remaining would be exported. Moreover, the power distribution network can accommodate the transformation with a slight increase in power distribution loss up to 1% and a modest reinforcement requirement owing to line overloading. Conclusively, this study revealed that the transformation and reinforcement requirement are significantly distinct from those required in cold regions. [ABSTRACT FROM AUTHOR]

• Design criteria for active solar technologies integration in the historic built environment to favor energy transition, cleaner energy production, and climate mitigation. • Comparison of international guidelines' recommendations, design and assessment criteria. • Common glossary for the integration of active solar technologies in cultural heritage. • Definition of clear and updated design criteria valid for traditional and innovative active solar systems. • Support to solar innovation for the development of ad hoc products that considered correct design criteria. The paper focuses on architectural integration of active solar technologies (photovoltaic and solar thermal systems) in the historic built environment. Interpreted and used as a building material in the envelope, they are able to positively support energy transition and to contribute to climate change mitigation without compromising the original typological-functional characteristics. Their integration is fostered by innovative architectural design, aesthetical appeal, flexibility, multi-functionality, cost reduction, and technological development. Moreover, they can enhance economic value, functionality, and human comfort of buildings. Despite these advantages, there is a social diffidence on their acceptability due to policy, design, technical, economic and information barriers. To overcome these issues, the paper aims at identifying shared design criteria for their integration in the historic built environment, through a detailed assessment of 44 international guidelines. The study develops a taxonomy of international recommendations on energy efficiency, microgeneration, and solar integration, missing in literature so far, by analysing and comparing recurring criteria and recommendations. Also, a glossary of the most important terms according to the aesthetic, technical, and energy integration levels is created. Thus, the research defines an innovative framework of reliable design criteria, and a shared vocabulary to promote active solar technologies integration in the historic built environment. [ABSTRACT FROM AUTHOR]

Strict energy performance requirements have reduced the building's operational energy use and related environmental impact in the past decades. Consequently, the embodied impacts of buildings gained importance, and hence focussing on the life cycle impacts has become essential to further reduce the environmental impact of buildings. Life cycle assessment (LCA) is an internationally accepted method used for this purpose. Current LCA studies of buildings typically look at their whole life cycle but ignore potential changes in operational energy use due to climate change. However, the latter is expected to influence the operational energy use of buildings to a significant extent. This paper aims to improve the assessment of the operational phase by including variations in the yearly operational energy use caused by climate change and expected changes in the electricity mix as a strategy for climate change mitigation. This adapted dynamic assessment method is used to assess a Belgian office building. The results indicate that a Representative Concentration Pathway (RCP) 8.5 climate change scenario causes a decrease of 18 % for the end use of gas for heating and an increase of 32.4 % for the electricity use for cooling and ventilation. For the case study, the reduction of the environmental impact due to the decrease in gas use compensates the impact increase due to the higher electricity use. An increase in environmental impact in future would however occur if both heating and cooling are provided by electricity. Furthermore, depending on the changes that will occur in the electricity mix, the global warming potential of the office building will change by −13 % for an electricity mix dominated by renewables and by + 10 % for one with mainly production by gas compared to the situation with the current mix. However, the inclusion of a more extended list of environmental indicators shows that the overall environmental impact will only reduce by 4 % or even increase by 7 % compared to the situation with the current mix. [ABSTRACT FROM AUTHOR]

Decarbonization of urban water systems has been increasingly important for addressing the target of climate change mitigation. Yet, little insight has been derived regarding how water use and energy transition policies jointly alter climate impact of water systems in cities. In this study, using 21 cities in Guangdong Province (China) as a case study, we account for the carbon dioxide emissions related to energy use in urban water systems (UWSs) over 2006–2035. By constructing a city-scale water-carbon nexus model, we project future trajectories of electricity-related CO 2 emissions in UWSs by considering dynamics of water demand and varying pathways of energy transition. We show that increasing need of tap water purification is a main contributor to electricity-related CO 2 emissions, followed by tap water distribution and wastewater treatment. The electricity-related CO 2 emissions in most cities could peak before 2030 under a fast-decarbonization energy transition pathway, despite the rise of urban water demand. Fast-urbanizing cities like Foshan, Shantou and Qingyuan may witness a 4–23% increase of emissions between 2020 and 2035. With a more rapid increase of proportion of cleaner energies in electricity mix, the fast-decarbonization pathway could end up with 21% more reduction of emissions on average during 2020–2035, compared to currently-planned energy transition pathway. Under the stringent climate goal of China, cities should foster a joint mechanism between water system and grid companies that leads to a more effective low-carbon governance of water systems. [Display omitted] • We project carbon peaks in UWSs under varying energy transition pathways. • Tap water purification is a main contributor to CO 2 emissions in UWSs. • Fast-urbanizing cities may further increase their emissions during 2020–2035. • Fast-decarbonization pathway would mean 21% more reduction of emissions. [ABSTRACT FROM AUTHOR]

• We attained significant energy reduction potentials by energy-efficient renovations. • Large-scale implementation of heat pumps decreased the total delivered energy. • Energy savings ranged from 2 to 9% in 2030, 2–14% in 2040, and 2–19% in 2050. • Energy efficient building stocks alleviated the need for increased energy capacity. • New constructions should follow low-energy-use standards. A geo-referenced building stock model was used to analyse the energy and climate performance of the Knowledge Axis in Trondheim, Norway, by 2050. Strategies for energy upgrades, construction of more energy-efficient new constructions, changes in heating technologies, and their implications in terms of energy savings and greenhouse gas (GHG) emissions associated with energy and materials are assessed through various scenarios. Thematic maps were used to display the development of the total floor area and energy use. Compared to the baseline scenario for the same year, the energy savings range from 2 to 9%, 2–14%, and 2–19% in 2030, 2040, and 2050, respectively. New passive house constructions combined with energy upgrades in renovation projects and the maximum use of heat pumps have the greatest energy-saving potential. Our results displayed a large variation in the total net GHG emissions as a result of the alternative emission factors. The total net GHG emissions are primarily affected by the energy savings (amplified by assuming fossil fuel as the marginal mix), electricity mix (Norwegian or European), and allocation chosen for the incinerated waste to feed the district heating system. [ABSTRACT FROM AUTHOR]

• Power to heat is an effective option to decarbonize endothermic chemical processes. • Induction heating, Joule heating, Microwave heating and RDR reactor reviewed. • Open challenges and perspectives for scale-up and implementation of these systems are analyzed. The rising environmental problems and demanding actions aimed at mitigation of climate change are calling for a radical transformation of different industrial sectors. In this perspective, chemical process industry is called to play a major role towards this transition, on one hand by transforming its existing carbon-intensive processes and, on the other hand, providing clean alternatives for hard-to-decarbonize applications. Among the possible options for the transformation of catalytic processes, power-to-heat is considered in view of large available quantities of green electricity. This approach is considered industrially-mature and can be applied at a large scale for a wide number of existing processes, providing substantial advantages in terms of intensification. The aim of this work is to provide a detailed analysis of the status quo of these technologies and critically discuss them towards their possible use for a generic process of interest by the identification of several key indicators. The main technologies proposed i.e microwave heating, joule heating, induction heating and roto-dynamic reactor are reviewed alongside with some of the applications already reported in open-literature. Afterwards, a critical analysis about material needs, energy efficiency of the proposed methodologies, the possibilities of scale-up and retrofitting and the impact on the overall process design and performances are reported. [Display omitted]. [ABSTRACT FROM AUTHOR]

The global methanol industry provides a unique supply chain to a plethora of chemicals and processes while also contributing to high quotas of greenhouse gas (GHG) emissions. Hence, as the world focuses on climate change mitigation, green technologies are emerging, allowing for low carbon operations. However, the timeliness and scalability of these technologies limit the sustainable transition of the current methanol industry. Thus, to promote low carbon operations within business-as-usual methanol production, innovative technologies need to be investigated-boasting high resource and energy efficiencies. Here, we examine the effectiveness of novel gas-heated reforming (GHR) over autothermal (ATR) and steam reforming (SMR)- coupled with carbon capture and storage platforms (CCS), in promoting sustainable methanol operations. Our results highlight the GHR as the most efficient flowsheet- with 11 % overall higher energy efficiency and 17 % increase in resource efficiency over ATR and SMR processes. Furthermore, through coupled CCS and electrification, energy and resource efficiencies were improved with GHR systems- achieving 14–41 % reduced GHG emissions compared to ATR and SMR flowsheets. Lastly, considering process economics, GHR flowsheets outperform- producing minimum selling prices up to 24 % lower than the current market. Ultimately, as the world seeks to promote a sustainable transition through the deployment of low carbon energy sources, GHR systems emerge as the preferred technologies- providing both economic and environmental benefits for future low carbon methanol operations. [Display omitted] • Methanol production through Gas-Heated Reforming (GHR) was investigated. • GHR gives 8 % higher energy efficiency over ATR and SMR systems. • Low-carbon electrification sources reduced GHG emissions by 71 %. • CCS integration further lowers GHG emissions by 44 %. • Overall sustainable operations favours GHR and GHR+CCS systems. [ABSTRACT FROM AUTHOR]

The global increasing trend in total greenhouse gas emissions in recent decades has triggered the need for climate-related development financing. This study analyzes the effects of climate-related development mitigation finance and renewable energy consumption on greenhouse gas emissions in the Congo Basin. Using panel data from 2002 to 2020, panel regression estimates empirically reveal (1) a minimal significant increase in greenhouse gas emissions with respect to an increase in climate-related development mitigation finance (2) An increase in climate-related mitigation finance significantly promotes the consumption of renewable energy (3) An increase in renewable energy consumption reduces greenhouse gas emissions (4) An increase in renewable energy consumption reduces the effect of climate-related mitigation finance on greenhouse emissions. This study suggest the continuous flow of climate-related development mitigation finance from donor countries and bodies to developing countries on a more regular basis and the putting in place of a mechanism that tracks climate funds to ensure that they are effectively used in generating renewable energies such as solar, hydroelectric, biomass, wind and geothermal energies. • Climate-related development finance and renewable energy consumption in GHG emissions reduction in the Congo Basin. • Climate-related development mitigation finance increases renewable energy consumption and GHG emissions significantly. • Renewable energy consumption triggers a decline in greenhouse gas emissions. • Climate-related development finance reduces GHG emissions through increased renewable energy consumption. • The energy consumption structure is transformed from non-renewable to renewable energy due to climate mitigation finance. [ABSTRACT FROM AUTHOR]

• Hybrid bottom-up model to integrate geo-referenced and synthetic stock models. • Demonstrate multi-scale and long-term transitional workflow for commercial building stock. • Identify drivers and determinants of energy usage in the commercial building stock. • 32% cumulative performance gap if concurrent consideration of physical and technical factors is disregarded. • Introducing the unit block concept to determine minimum viable requirements. For energy modelling of commercial building stock (CBS), several methodologies were developed focusing on technology alternatives and retrofits for climate change mitigation, but they often have limitations to involve a differentiated description of practices and policies across a range of scales and sectors. This paper presents a novel hybrid model by integrating spatial and synthetic modelling approaches to facilitate the concurrent consideration of multiple building-oriented elements. The model is further implemented on Japanese building stock to demonstrate multi-scale and long-term framework for energy modelling of CBS. The results indicate the following findings: the scale choice introduces significant variability in energy consumption estimation with a relative error of 10–17% mainly depending on building geometry and plug loads; the long-term model exhibits a higher scale-bounded uncertainty in terms of energy saving potential that varies by 2–3 times when a non-representative scale is applied; disregarding physical and technical factors results in a cumulative performance gap of up to 32%; and the accuracy and extent of the model across the stock are more sensitive to technical factors, such as building system stock dynamics and the heterogeneity of HVAC systems, than physical factors. The model provides a multi-tier framework using spatial intelligence building stock approach to develop long-term energy efficiency monitoring strategies for CBS. [ABSTRACT FROM AUTHOR]

Energy efficiency in buildings remains an important priority for the European Union. Towards that direction, therole of the one-stop-shops (OSSs) that will offer integrated renovation services has been further enhanced. Thepaper aims to compare public and private-driven OSSs to increase knowledge regarding the way they operate.The business models of ten OSSs for renovation of residential buildings, operating in six European countries havebeen analysed, and further evaluated for their engagement in the critical moments of the renovation customerjourney. Findings show that the majority of examined OSSs have managed to reach some level of standardizationin the way they deliver their service. Additionally, the examined OSSs appear to be engaged in the critical momentsof the renovation customer journey, which can attract the interest of potential customers. Public-drivenOSSs appear to operate smoother in the renovation market and offer services that are considered crucial for theirpotential customers. Their viability though in the absence of public funding needs to be further examined. On theother hand, private-driven OSSs are called to deal with greater challenges in their effort to consolidate their presencein the renovation market. Providing access to financing remains a challenge for private-driven OSSs, whichto attract more customers resort to value additions to their service, which might affect their viability. The studyprovides valuable information and insights about OSSs in operation that could be useful to stakeholders that areactive in the renovation market or interested to enter it. • Analysis of ten one-stop-shop business models for residential building renovations operating in six European countries. • Operational modality of public and private driven OSSs has been critically reviewed and compared. • Public-driven OSSs appear to operate smoother in the renovation market offering services that are considered crucial by customers. • Private-driven OSSs are dealing with greater challenges in trying to consolidate their presence in the renovation market. • Private-driven OSS build trust with their customers by engaging in specific "critical moments" of the renovation journey and by adding value to their service. [ABSTRACT FROM AUTHOR]

To illustrate the power and utility of macro-level decomposition tools, this article presents a structured comparison of two all-sector global modeling exercises that assess emissions reductions compatible with climate stabilization at roughly 1.5C above pre-industrial levels. It uses an expanded Kaya Identity combined with the LMDI (Logarithmic Mean Divisia Index) method to decompose the effects of key drivers of changes in emissions over time in these scenarios. The most important drivers of emissions reductions include final energy intensity of economic activity, the fraction of primary energy delivered by fossil fuels, and emissions from non-CO 2 warming agents. Land-use change and the carbon intensity of fossil energy are also important. The article suggests additional data modelers should release to allow more rapid analysis of results and ways to facilitate cross-study comparisons (such as adopting "best of breed" sectoral models instead of relying solely on in-house expertise for model development). Global change; Climate change; Emissions reduction modeling; Model comparisons; Energy resources; Environmental policy; Environmental technology; Energy Policy. • Detailed decomposition analysis gives visibility into key drivers and model structure. • Key drivers include energy intensity, fossil fuel fraction, and non-CO 2 warming agents. • Analyzing "edge cases" can help suggest superior technology/policy combinations. • Analyzing "edge cases" can help demonstrate the limits of feasible climate mitigation. • "Best of breed" sectoral models are an essential complement to IAMs. [ABSTRACT FROM AUTHOR]

• A new methodology for energy use and peak load forecasting is developed. • Forecast of total energy use show a decrease between 2 and 11 TWh towards 2050. • Indicators for evaluating the difference between energy forecasts and statistics. • Large differences in how well forecasts predicted energy use towards 2020. • Calibration of energy use models on multiple years may improve energy forecasts. Projections of future energy use in buildings are a crucial tool in the tracking and attainment of political targets for energy efficiency and climate gas mitigation. In this article, a new methodology for projecting both the final energy use and the peak power demand for the Norwegian building stock is presented. The novelty of the methodology is to combine a set of existing, previously documented models in a novel way that integrates building stock models, hourly energy demand load profiles, and energy system modelling. The result is a coherent long-term projection of both annual and hourly energy use for different energy carriers, presented here with four scenarios of final energy use. The results show an expected decrease in total energy use for the Norwegian building stock between −2 and −12 TWh towards 2050, corresponding to a −3% to −14% of the energy use in 2020. Models for projecting future energy use are helpful both to evaluate the potential effects of current policies and to help reveal the need for new or updated policies. However, to have the desired effect, the projections must be as realistic as possible and reflect the actual development in energy use in the building stock. This necessitates a methodology for evaluating historical long-term annual energy use projections to understand why some models succeed in predicting energy use development while others fail. In this article, a set of indicators for evaluating the calibration of different models are presented. The indicators evaluate the initial difference and the divergence in the annualised trend for energy use projection models, compared to statistical data. The indicators are used to compare selected historical energy use projections for the Norwegian building stock against energy use from statistics from 2000 to 2020. The comparison shows large differences between the different projections, where calibrated scenarios show energy savings that tend to be more optimistic in the reference projection but more conservative in the best case potential. [ABSTRACT FROM AUTHOR]

[Display omitted] • Deep decarbonisation requires a transformation of the capital stock. • The mutualistic capital–energy relationship is mapped for the US economy. • US capital assets have grown since ∼2004 without increased primary energy use. • Investment in lower energy-intensity intellectual property products has grown. • Energy-intensity of capital use fell in the residential and manufacturing sectors. Energy in economies is required both to build the stock of capital assets, and to produce goods and services from use of the stock. Decarbonization involves: changing the capital assets for producing energy; building and using capital with greater energy efficiency; and changing the distribution and characteristics of asset types in the capital stock. From 2004 to 2019, US capital assets grew by 24%, while primary energy consumption remained relatively unchanged. Here we examine the relationship between capital and energy in the US economy to understand how this growth occurred without increased energy use. The method involves mapping of energy use and greenhouse emissions onto four broad categories of capital stock, employing environmentally-extended input–output analysis and time-series data from a variety of sources. Result show that intellectual property products, with an energy intensity of 2.9 TJ / $million of investment, grew as a proportion of the capital stock relative to structures (7.0 TJ / $million) and equipment (6.9 TJ / $million). Meanwhile the energy intensity of using capital decreased in the residential sector and in all nineteen manufacturing sectors. The study connects strategies for decarbonization of the US economy in a concise and consistent framework, with turnover of the capital stock providing opportunities for both energy efficiency and decarbonization. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) emissions from energy consumption have become a major focus all over the world. Cities, especially, are playing an important role in responding to the challenges of global climate change. However, there is still a lack of case studies on the whole life stage (including energy import, production, and consumption) at the city level. This is the first study to systematically estimate the whole lifecycle GHG emissions of urban energy consumption in Macau using the life cycle assessment (LCA) method. It identifies the main contributors, draws the balance framework diagram of energy GHG emissions, and proposes the effective reduction measures through a scenario analysis. The results show that the total GHG emissions of energy consumption in Macau increased from 2.10 × 103 kt CO 2 eq. in 2000 to 5.34 × 103 kt CO 2 eq. in 2018. In 2018, the energy-related GHG emissions in the import phase, production phase, and consumption phase were 3.37 × 103, 4.08 × 102 and 5.34 × 103 kt CO 2 eq., respectively. Since the return of Macau from Portugal to China (December 20, 1999), its GHG emissions from energy import have come to depend more on Mainland China, increasing from 71% in 2000 to 98% in 2018. For the consumption phase, indirect GHG emissions are more important, accounting for 75.3% of total GHG emissions in Macau. From the final consumption perspective, the gambling industry results in the largest GHG emissions (38.07% in 2018), followed by the transportation industry; while for the final energy consumption types, electricity consumption has become the predominant source. The scenario analysis shows that under an ideal scenario, the GHG emissions can be slowed down and will peak in 2033. [Display omitted] • It firstly estimates the whole lifecycle GHG emissions from Macau urban energy consumption. • The GHG emission dependence on the mainland China increases from 71% to 98% since 2000. • It draws the balance framework diagram of energy GHG emissions. • Gambling industry results in the largest GHG emissions in Macau. • Under ideal scenario, the GHG emissions will peak in 2033. [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]

• Energy upgrade is promoted by interprovincial coordinating of economy and resource. • Energy upgrade is determined by change of farmers' preference for low-quality energy. • Energy upgrade can be promoted together in agriculture and rural life. Rural energy upgrade in developing countries is an essential part of the world climate mitigation work. To systematically promote rural energy upgrade, we took the case of China and used a spatiotemporal model to investigate the spatiotemporal characteristics of energy ladders in agriculture and rural life of developing countries. The proportion of clean energy consumption in agriculture showed an interprovincial competition effect, whereas the proportion of oil consumption in rural life showed a demonstration effect. Severe reliance on coal hinders energy upgrade. Urbanisation has improved the clean level of energy use in agriculture but does not significantly affect the in rural life. Technological progress has increased the proportion of clean energy consumption but has little impact on the proportion of oil and gas consumption. The abundance of coal resources has prompted the massive use of coal and coal-fired power. Energy-use habit promotes the use of all types of energy. Except for that of gas, the consumption of different types of energy in agriculture and rural life mutually promotes. In summary, the development of energy technologies should be established based on the regional resource endowment and economic development level. Also, subsidy principles for clean energy projects should be established based on the development level of the agriculture and residents' income. Rural energy upgrade is coordinated with urbanisation and the modernisation of the agriculture and rural life styles while paying attention to the mutual promotion between the agriculture and rural life. During the change of the energy-use habit under policy guidance, attention must be paid to interprovincial cooperation. Finally, a reasonable market mechanism should be established to promote energy upgrade. [ABSTRACT FROM AUTHOR]

The world needs to rapidly reduce emissions of carbon dioxide (CO 2) emission to stave off the risks of disastrous climate change. In particular, decarbonizing U.S. manufacturing industries is particularly challenging due to the specific process requirements. This study estimates the potential for future CO 2 emission reductions in this important sector. The analysis is a detailed accounting exercise that relies on estimates of emission-reduction potential from other studies and applies those potentials to the manufacturing sector using a bottom-up approach. The actions are grouped into four "pillars" that support deep decarbonization of manufacturing (DDM): Energy Efficiency, Material Efficiency, Industry-Specific, and Power Grid. Based on this bottom-up approach, the analysis shows that an 86% reduction in carbon dioxide emissions from the Reference Case is feasible. No single pillar dominates DDM, although opportunities vary widely by sub-sector. The analysis shows that a strategy incorporating a broad set of elements from each pillar can be effective instead of relying on any single pillar. Some pillars, such as Energy Efficiency and Material Efficiency, have wide applicability; others have key niche roles that are Industry-Specific; the Power Grid pillar requires interaction between grid decarbonization and industry action to switch from fossil fuels to zero-carbon electricity where appropriate. [Display omitted] • Four technological strategies ("pillars") for deep decarbonization of U.S. manufacturing are investigated. • Seven energy-intensive and one non-energy-intensive industries are examined separately. • An 86% reduction in energy related CO 2 emissions is feasible in U.S. manufacturing by 2050. • No single pillar dominates implying a diversified approach to decarbonization is needed. • Some emissions remain in select industries, in particular chemicals. [ABSTRACT FROM AUTHOR]

The uptake of actions to mitigate climate change at the household level might crowd out subsequent policy at the national level, which is problematic because national policy often has a larger mitigation potential than individual household measures. This study analyses crowding out between the uptake of low-cost actions and the support for national climate change policy in the agricultural sector. In the experimental set-up, survey respondents were primed to think about the implementation of low-cost mitigation practices and subsequently asked to express support for national mitigation policy. The results show a crowding-out effect between individual mitigation measures and support for national policy. Individuals with high levels of worry show a stronger crowding out effect. This study contributes to building understanding of when and why crowding out occurs in order to help frame and communicate future climate change policy. • Individual mitigation measures crowd out support for national climate change policy. • Crowding out is stronger within the climate change domain than biodiversity domain. • Individuals with high levels of worry show a stronger crowding out effect. [ABSTRACT FROM AUTHOR]

In view of advancing global warming, climate change adaptation and mitigation measures for buildings are becoming increasingly important. The following article discusses the question of whether current urban planning principles still provide a suitable framework for this purpose. Therefore, the aim of this paper is to identify and quantify the main parameters of the urban planning framework regarding energy demand and energy generation of buildings. In a novel, holistic approach, passive design measures were evaluated in a joint analysis based on a case study in the city of Vienna (Austria) with respect to their impact on heating and cooling energy demand, overheating potential, daylight availability, and solar potential of new buildings. The results show that by improving the thermal insulation of the building envelope, the importance of a high compactness of the buildings to lower the heating energy demand is decreased. In addition, moderate compactness can help reduce the energy demand for space cooling and artificial lighting and increase the solar potential of the buildings. Accordingly, the results question the dogma of a high degree of compactness that still exists to some extent and indicate that changing climatic conditions also require new approaches in the urban planning process. [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]

• Energy Hub optimization of asset design and operation to supply data-driven demand. • Novel comparison of distinct asset sets regarding total cost and LCA emissions. • Purely stationary asset upgrades are preferential to pure mobility upgrades. • Joint optimization of MES and EVs enables large CO 2eq mitigation at negative costs. • Uncertainty analysis highlights robust benefits of cross-sector optimization. Residential energy and mobility demand are responsible for a substantial share of global greenhouse gas emissions due to high dependency on fossil fuels in heating and motorized individual transport. Technology upgrades might enable cost-effective climate change mitigation in decentralized mobility-including Multi-energy Systems (MIMES). Their holistic and cross-sectoral optimization with respect to design and operation can innovatively identify tradeoffs between ecological and economical solutions, quantify potential benefits and show drawbacks over current solutions. To this end, this work provides a novel, hourly-resolved, consumer-centric, multi-objective optimization framework based on the Energy Hub concept that includes private mobility investments. It compares four distinct technology portfolios for minimal lifecycle emissions and total annualized cost when supplying residential demands to promote beneficial solutions. In a case study, consumers may modify their combustion-based heating and mobility systems (1) by switching to e-mobility (2), by switching to stationary Multi-energy Systems (3), or by switching to e-mobility and Multi-energy Systems simultaneously (4). Optimizations on 83 stochastically selected single- and multi-family buildings in St. Gallen, Switzerland, demonstrate that all three technological upgrades offer improved performance over (1): While costs decrease moderately in cost-driven optimizations, emission reductions range from 16% up to 68% when emission-driven optimizations are performed. The joint electrification of stationary and mobile assets (4) is particularly attractive and outperforms all other technology cases. Scenario (3) offers the second-best performance. While the optimal design and operation of assets depend on the technology availability, emission reduction targets, building size, and demand properties, an uncertainty analysis underpins the overall benefits of upgrades and accredits robustness to abovementioned ranking for wide techno-economic parameter variations and objectives. Pathways for further emission reductions are additionally shown, and the increased initial cost and seasonal dependence on the electricity grid are discussed as hurdles for a widespread implementation of economical and climate-friendly MIMES. [ABSTRACT FROM AUTHOR]

The impact of energy use on the planet due to related CO 2 emissions is continuously increasing, despite the adoption of efficiency and decarbonisation policies and widespread environmental awareness. Climate change mitigation will only succeed if the driving forces of consumption and emissions are deeply analysed, and effective means are provided to reverse their trends. To this aim, the Kaya Identity framework is revisited to classify indicators and decomposition studies in the literature. A comprehensive pyramid approach is proposed for the progressive disaggregation and discussion of energy and emissions changes. The approach is applied to the OECD and non-OECD to provide meaningful regional analysis of past trends and future projections according to stated policy intentions. Results show that a hopeful change has already begun in the developed region due to a sustained decrease of the energy intensity and a promising reduction of the carbon intensity. Emerging economies follow the performance of developed nations since 2013, held back by later economic development. Activity slowdown, energy conservation, renewable electrification, efficient power plants and coal phase out appear as the keystones for decarbonisation. As a result, emissions stabilisation could have already been achieved as rises in emerging countries are offset by drops in developed nations. However, more stringent climate policies, especially targeting carbon drivers, are urgently needed to enable emissions reductions compatible with a global temperature increase of 1.5°C. • Classification of indicators and studies in the framework of Kaya Identity. • Definition of an Emissions Indicators Pyramid for analyses standardisation. • Activity slowdown and cleaner electricity drive emissions drop in the OECD since 2007. • Hopeful efficiency and carbon intensity changes in developing nations since 2013. • Energy intensity must be halved and carbon intensity quartered in 2040. [ABSTRACT FROM AUTHOR]