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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.

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.

Abstract In this paper, the problem of the optimal rating of transformers in smart grids is properly discussed with respect to the specific load characteristics. The design is based on the accurate prediction of the lifetime degradation of mineral-oil-immersed transformers subject to highly intermittent loads. In fact, by investigating the nature of the loads in the smart grid scenario, it clearly appears that the intermittent nature of the power demand increases drastically. In this context, specific tools for the characterization of the lifetime duration are required, since severe reduction of the transformer’s life can be observed due to overloads, even in the case of short-duration overloads. The classical approaches based on using the equivalent thermal current to predict the transformer’s lifetime might result in incorrect estimates, thus requiring advanced models that can deal with the time variability of the load. In this paper, the randomness of the load powers is addressed in terms of stochastic processes. In particular, the Wiener process is demonstrated to provide robust modeling of load variability. By starting from this assumption, it was demonstrated analytically that the hot-spot temperature, which is a major contributor to the degradation of the transformer’s lifetime, also is a stochastic process. Then, in spite of the nonlinearity of the thermal model, the hot-spot temperature can be represented as a Wiener process, the robustness of which has been verified adequately. By taking into account the nonlinear relationship between the hot-spot temperature and the lifetime, the authors verified that the transformer’s lifetime is modeled as a lognormal, stochastic process. Hence, a novel, closed-form relationship was derived between the transformer’s lifetime and the distributional properties of the stochastic load. The usefulness of the closed-form expression is discussed for sake of design, even if a few of the considerations also are performed with respect to operating conditions. The aim of the numerical application was to demonstrate the feasibility and the easy applicability of the analytical methodology.

An efficiency evaluation of China’s regional sustainable innovation, evaluating industrial waste and total energy consumption, is the main research subject in this paper. It focuses on a regional measurement and comparison of these undesirable outputs of Chinese firm activities, such as industrial SO2 and CO2 emissions. By applying a data envelopment analysis–slack-based measure (DEA–SBM) model with undesirable outputs indicators, the regional innovation efficiency was evaluated for 30 provinces in China, from 2002 to 2014. The results indicate that the sustainable innovation efficiency of overall China is still relatively low, and varies significantly in different regions. Central and Western China have similar sustainable innovation efficiencies, which are much lower than the sustainable innovation efficiency in Eastern China. Furthermore, the data indicate that regional sustainable innovation efficiency disparities among these three areas are decreasing. Based on these findings, reasons for the sustainable innovation efficiency gap among the different regions were analyzed. To scholars, this paper extends the research on regional sustainable innovation efficiency by implementing an undesirable output perspective to the DEA–SBM model. The findings also provide Chinese policy makers with useful decision support insights for regional sustainable innovation, and energy conservation and emission reduction policies.