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PurposeThe pressure of globalization has raised social concerns related to the protection of the environment, forced companies to use sustainability as a strategic weapon to fulfill the legal obligations and achieve overall competitiveness. It is reported that small- and medium-sized enterprises (SMEs) are globally responsible for approximately 70 percent of the industrial pollution, justifying urgent attention to the operations of these businesses. The purpose of this paper is to analyze the impact of sustainability orientation (SO) and supply chain (SC) integration implemented by SMEs on their sustainable procurement (SP) and design. Moreover, this study examines how SMEs’ SP and design affect their environmental and cost performance (CP).Design/methodology/approachThe authors develop a comprehensive model to test the relationships among SC, SC integration, SP, sustainable design (SD), environmental performance (EP) and CP at the SMEs level. The authors investigate the relationships of the mentoned factors by a data set that is collected from 358 Indian manufacturing SMEs.FindingsThe results indicate that in the SMEs’ context: SO positively influences both SP and SD; external integration positively affects SP; internal integration positively affects SD; SP positively influences EP and has not impact on CP; and SD positively influences both EP and CP.Originality/valueThis study provides a broad view of the relation between driving factors that may direct SMEs toward a better sustainability performance and offers practical managerial insights into these important business entities.

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.

This work analyzes gendered processes by a methodology based on clustering factors with influence in the decision-making process of women as users or employees of the transport system. Considering gender as a social construction which changes over time and space, this study is based on the concept of a woman as a person who adopts this role in society. This paper performs a deep analysis of those factors women consider as needs and barriers to use or work in the transport system in four scenarios: railway public transport infrastructures, automated vehicles, bicycle sharing, and jobholders. A literature review and focus group discussions were performed under the consideration that the definition of woman includes the addition of several personal characteristics (age, sexual orientation, family responsibilities, and culture). The data analysis allowed the identification of fairness characteristics (FCs) that affect the interaction of women with the transport system for each scenario. A methodology for clustering the fairness characteristics identified the main areas of action to improve the inclusion of women within each use case. Further studies will be focused on the quantification and prioritization of the FCs through mathematical methods and the suggestion of inclusive measures by an interdisciplinary panel.

Digitalization provides opportunities for sustainable development. Cultivating postgraduates’ digital skills is an important task of higher education to support sustainable development (HESD). As a crucial way of cultivating digital skills, high-quality online learning processes are of great significance to achieve “Quality Education”, in line with the 2030 sustainable development agenda. Based on Biggs’s 3P (Presage-Process-Product) learning model, this study focused on the whole learning process and explored the relationship among postgraduates’ information literacy, online platforms, online knowledge-sharing processes and their innovation performance. The analysis of a questionnaire survey of 501 Chinese postgraduates showed that (1) information literacy has a positively predictive effect on postgraduates’ innovation performance; (2) different online learning processes lead to different learning results. Compared to the quantity-oriented online knowledge sharing process (Qty-KSP), the quality-oriented online knowledge sharing process (Qlty-KSP) is related to better innovation performance, which opens onto this study’s third finding: (3) Qty-KSP and Qlty-KSP play a parallel mediating effect between postgraduates’ information literacy and their innovation performance. Compared to Qty-KSP, Qlty-KSP is a more powerful intermediary variable, which leads to this study’s fourth finding; (4) an efficient online learning environment can contribute to higher-quality online learning process, thus improving postgraduates’ innovative performance. This study suggests that policy makers should develop postgraduates’ digital skills for sustainable development in the digital age. This can be achieved by (1) cultivating postgraduates’ information literacy; (2) encouraging them to practice high-quality online learning processes; and (3) providing an efficient sharing platform for sustainability, resilience, and digitalization in higher education.

Impacts in the form of innovation and commercialization are essential components of publicly funded research projects. PHUSICOS ("According to nature" in Greek), an EU Horizon 2020 program (H2020) Innovation Action project, aims to demonstrate the use of nature-based solutions (NBS) to mitigate hydrometeorological hazards in rural and mountainous areas. The work program is built around key innovation actions, and each Work Package (WP) leader is specifically responsible for nurturing innovation processes, maintaining market focus, and ensuring relevance for the intended recipients of the project results. Key success criteria for PHUSICOS include up-scaling and mainstream implementation of NBS to achieve broader market access. An innovation strategy and supporting tools for implementing this within PHUSICOS has been developed and key concepts forming the basis for this strategy are presented in this research note.

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.