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A continuous growth of international trade, especially between developing countries, has greatly increased carbon dioxide (CO2) emissions associated with energy consumption over the past two decades. Given the more intensified intraregional cooperation and trade within the Belt and Road Initiative (BRI), this study aims to trace the imbalance of CO2 embodied in trade between nations in BRI and the rest of the world, providing new insights into the drivers of emissions growth by contrasting consumption, production and technological differences-based perspectives. Results indicate that the BRI contributed to over 50% of global carbon footprint and 92% of its increase in 1995–2015. The BRI was a net exporter of trade-embodied emissions, whose technological-adjusted carbon footprint remained remarkably large due to comparatively high carbon intensity. Geographically, carbon leakage has gradually moved from China and India to other BRI countries, especially to Southeast Asia, West Asia and Africa. Technological change was the key driver of emissions reduction, followed by the change in industrial structure. The growth in final demand per capita was the most important driver for the growth of CO2 emissions in BRI. Improving carbon efficiency remains a critical step for BRI nations to slow down not only emissions growth but also carbon leakage. The paper managed to provide novel insights into the carbon leakage in BRI by contrasting the consumption, production and technological differences-based perspectives, thus being able to better inform policymakers on region-specific low-carbon transition and global climate governance.

Abstract The issues dealing with climate change also caused by the private transport sector are attracting more and more attention in European Countries. This work aims to simultaneously address the issue related to the management of the energy demand of the private transport sector and building sector in the framework of the smart grid energy districts. The investigated smart grid is designed for meeting the energy demands of a district, including the energy demand for space heating and cooling, as well as the electric energy of a lot of buildings, occupied by people who only use electric vehicles. The system is equipped with a cogenerator, which is expected to operate according to the base-load operation strategy, constantly producing the rated power for whatever power demanded. Two charging strategies are analyzed and compared with the aim of detecting which strategy better exploits the power produced by the cogenerator. The performed dynamic simulations of the whole system are carried out by means of TRNSYS tool. TRNSYS allows one to use a detailed library of components which carefully model and simulate the devices included in the proposed system model. Suitable control strategies are also developed in order to improve the energy, environmental and economic performance of the smart grid. The results show that this layout allows one to considerably reduce the primary energy consumption and carbon dioxide emissions of the investigated district. As matter of fact, the proposed smart energy grid achieves a primary energy saving index of about 32%, with a payback period of about 6 years. These promising results suggest that the proposed smart energy grid may be useful for addressing the issue related with environmental impact of building sector and private transport sector.

Daily operations and planning in a smart grid require a day-ahead load forecasting of its customers. The accuracy of day-ahead load-forecasting models has a significant impact on many decisions such as scheduling of fuel purchases, system security assessment, economic scheduling of generating capacity, and planning for energy transactions. However, day-ahead load forecasting is a challenging task due to its dependence on external factors such as meteorological and exogenous variables. Furthermore, the existing day-ahead load-forecasting models enhance forecast accuracy by paying the cost of increased execution time. Aiming at improving the forecast accuracy while not paying the increased executions time cost, a hybrid artificial neural network-based day-ahead load-forecasting model for smart grids is proposed in this paper. The proposed forecasting model comprises three modules: (i) a pre-processing module; (ii) a forecast module; and (iii) an optimization module. In the first module, correlated lagged load data along with influential meteorological and exogenous variables are fed as inputs to a feature selection technique which removes irrelevant and/or redundant samples from the inputs. In the second module, a sigmoid function (activation) and a multivariate auto regressive algorithm (training) in the artificial neural network are used. The third module uses a heuristics-based optimization technique to minimize the forecast error. In the third module, our modified version of an enhanced differential evolution algorithm is used. The proposed method is validated via simulations where it is tested on the datasets of DAYTOWN (Ohio, USA) and EKPC (Kentucky, USA). In comparison to two existing day-ahead load-forecasting models, results show improved performance of the proposed model in terms of accuracy, execution time, and scalability.

Abstract Transitioning to sustainability will require innovation, not just in technological and economic terms but also in governance and culture. The work presented here sought a remote, vulnerable island context (Fiji, South Pacific) to enable further insight into the innovation process related to resilience and sustainability. The innovation targeted by this work was off-grid solar renewable energy (RE) systems framed as a development instrument to promote local, community-based resilience to climate change through increased livelihood security and reduction of climate change effects. Applying a local-level resilience framework to solar technology use, we conclude that the RE system can improve resilience, however, unintended consequences included a rush for energy usage causing a “tragedy of commons” of finite stored energy and subsequently increased supplementary fossil fuel use. This suggests that there are still missed resilience opportunities in the way that the innovation is implemented in developing countries and remote areas in particular. Further analysis demonstrated that improved planning at the socio-technological interface has the potential to strengthen communities' resilience. With significant RE investments required for a transition to a low-carbon future in many developing countries, there is a pressing need to effectively introduce innovative uses of technologies. Existential threats to many local communities, and some nations in the Pacific may mean that sub-optimal innovation will not be enough.

The challenges of sustainable development (and climate change and peak oil, in particular) demand system-wide transformations in sociotechnical systems of provision. An academic literature around coevolutionary innovation for sustainability has recently emerged as an attempt to understand the dynamics and directions of such sociotechnical transformations, which are termed ‘sustainability transitions’. This literature has previously focused on market-based technological innovations. Here we apply it to a new context of civil-society-based social innovation and examine the role of community-based initiatives in a transition to a low-carbon sustainable economy in the UK. We present new empirical research from a study of the UK's Transition Towns movement (a ‘grassroots innovation’) and assess its attempts to grow and influence wider societal sociotechnical systems. By applying strategic niche management theory to this civil society context, we deliver theoretically informed practical recommendations for this movement to diffuse beyond its niche: to foster deeper engagement with resourceful regime actors; to manage expectations more realistically by delivering tangible opportunities for action and participation; and to embrace a community-based, action-oriented model of social change (in preference to a cognitive theory of behaviour change). Furthermore, our study indicates areas where theory can be refined to better explain the growth and broader impacts of grassroots innovations—namely, through a fuller appreciation of the importance of internal niche processes, by understanding the important role of identity and group formation, and by resolving how social practices change in grassroots innovations.

Abstract Polygeneration is a sustainable energy concept, which could deliver multiple energy vectors (power, heat and cooling) and other useful products (desalinated water, hydrogen, etc.) by possible integration of suitable technologies. In this study, a polygeneration system for coastal area applications is driven by a hybrid solar/bio-oil boiler to produce the energy intensive products of cooling, power, desalinated water and hot air for drying. Solar thermal is the primary energy source to drive the polygeneration system, whereas the bio-oil boiler is the auxiliary source to drive it at non-sunshine hours. The performance characteristics of the hybrid system is investigated to identify the effects of various key parameters such as temperatures of heat source, sink and evaporator along with expander Built in Volume ratio. The obtained results at a typical operating condition show that the parabolic trough collector aperture area is 130 m2, the system produces net power output of 47 kW, desalinated water of 1.205 m3/h, 92 kW of hot air for drying and 141 kW of cooling. The overall energy and exergy efficiencies of the proposed system are found to be 22% and 87%, respectively.

The paper interprets a smart grid as an enhanced service environment. For a reliable, autonomic integration of services without permanent actions of experts, the Service Discovery has to agree on the semantic interpretation of nonfunctional and technical criteria. The proposed service environment for smart grids applies our Service Discovery framework ACTAS (Adaptive Composition and Trading with Agents for Services), which is based on software agents and enhances SOC with ontologies for the classification and quasi-standardization of criteria and their constraints for a commonly agreed Service Discovery, Composition, and Selection taking advantage of established algorithms.