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Abstract Hydrogen is considered a potential game changer for world energy systems and a solution to climate change concerns, as it generates zero waste and it is suited for power generation and transportation. Despite its several advantages, there are significant technical challenges in deploying a stable hydrogen economy including improving its process efficiencies, lowering production costs, maintaining cost-effective transmission and distribution, and exploiting inexpensive and sustainable feedstocks. In this context, a detailed study was conducted to analyze the production sources, technologies, storage and transport systems, and global potential exportable feedstocks to produce hydrogen. A comprehensive analysis of current hydrogen production technologies with their energy efficiencies and hydrogen selling prices was reported in this study. Various hydrogen production technologies with their capital investments and CO2 emissions were also presented. Potential feedstocks for hydrogen production were identified and analyzed through a product space model, which characterizes a network of global exportable products based on their similarities and productive knowledge. It was established that the hydrogen production feedstocks and sources currently used are primarily available in six countries: the United States of America, France, Russia, Sweden, the Netherlands, and Spain. Broadly, the results revealed that the United States of America and Russia shared the highest hydrogen feedstock exports, indicating a higher probability of hydrogen production in these countries. Except for Russia, all the studied countries fell in the most desired quadrant, indicating that they can move in all product space directions to exploit unexplored hydrogen feedstocks for better sustainable economic growth.

SummaryEnvironmentally extended multiregional input‐output (EE MRIO) tables have emerged as a key framework to provide a comprehensive description of the global economy and analyze its effects on the environment. Of the available EE MRIO databases, EXIOBASE stands out as a database compatible with the System of Environmental‐Economic Accounting (SEEA) with a high sectorial detail matched with multiple social and environmental satellite accounts. In this paper, we present the latest developments realized with EXIOBASE 3—a time series of EE MRIO tables ranging from 1995 to 2011 for 44 countries (28 EU member plus 16 major economies) and five rest of the world regions. EXIOBASE 3 builds upon the previous versions of EXIOBASE by using rectangular supply‐use tables (SUTs) in a 163 industry by 200 products classification as the main building blocks. In order to capture structural changes, economic developments, as reported by national statistical agencies, were imposed on the available, disaggregated SUTs from EXIOBASE 2. These initial estimates were further refined by incorporating detailed data on energy, agricultural production, resource extraction, and bilateral trade. EXIOBASE 3 inherits the high level of environmental stressor detail from its precursor, with further improvement in the level of detail for resource extraction. To account for the expansion of the European Union (EU), EXIOBASE 3 was developed with the full EU28 country set (including the new member state Croatia). EXIOBASE 3 provides a unique tool for analyzing the dynamics of environmental pressures of economic activities over time.

The relationship between exchange rate volatility (ERV) and other macroeconomic factors, including trade flows, domestic production, inflation, money demand, and economic growth, has remained a topic of a large number of studies in international finance. However, the research question that the past empirics have overlooked is whether ERV has any role in helping an emerging economy like China in its journey toward a renewable energy transition. To answer this question, this analysis intends to scrutinize the nexus between ERV and renewable energy investment (REI) in China over 1991Q1-2021Q4. Moreover, due to the asymmetric behavior of ERV, we have based our analysis on the asymmetric assumption. For analyzing the short and long-run impacts of ERV on REI in China, we used the linear autoregressive distributed lag (ARDL) and nonlinear ARDL methods. In the long term, the ARDL model predicts that stricter environmental policies and higher GDP will lead to more investment in renewable energy. As far as the nonlinear model is concerned, the long-term negative change in ERV does not affect REIs, whereas a long-term positive change in ERV reduces such investments. Likewise, unfavorable short-term exchange rate shocks encourage REI while positive short-term shocks discourage such development. Moreover, investment in renewable energy is bolstered by GDP, environmental policy strictness, and financial development, but is dampened by CO2 emissions in the short term in both models. On the basis of these results, we suggest that policymakers should consider implementing measures to stabilize exchange rates to promote investment in renewable energy.

Abstract Purpose Investigating potential social and socio-economic impacts should play a key role for the development of sustainable mobility alternatives. Social life cycle assessment (S-LCA) is becoming increasingly important to ensure holistic sustainability assessments. The present work aims at identifying and evaluating social and socio-economic impact subcategories in S-LCA. A novel participatory approach implying all concerned stakeholders is proposed to select relevant impact subcategories and thus contribute to a thorough interpretation of S-LCA results. It is applied to assess electric and conventional vehicles. Methodology This paper describes a comprehensive step-by-step S-LCA framework. The innovation of this work consists in defining a structured S-LCA framework integrating a systematic approach based on two stages: (1) a sectorial risk analysis for the identification of impact subcategories and (2) a participatory approach for their prioritization. The proposed participatory approach considers all concerned stakeholders to enable the selection of the most relevant impact subcategories. A set of social inventory indicators is attributed to subcategories that were perceived as the most relevant. These are used to perform the social evaluation and carry out a full analysis in the result interpretation allowing thus to integrate a multi-actor perspective to the materiality assessment. Results The defined S-LCA framework is implemented to compare two mobility scenarios, corresponding to electric and conventional vehicle technologies. A new set of mobility-related impact subcategories is proposed for users’ stakeholder. Following the new designed participatory approach, subcategories for all stakeholders are prioritized according to different actors’ perceptions. For example, “safe and healthy living conditions,” “local employment,” and “delocalization and migration” were perceived for local communities as the most relevant subcategories by the different consulted stakeholders (industrial, academic, and public actors and users). These results also showed that social significance varies depending on the consulted actors and on the geographical area of the study. Using PSILCA database, we have investigated the subcategories that were perceived as the most relevant. Results for the evaluation and interpretation phases are presented for both transportation technologies. Conclusions This approach aims at increasing local relevance of S-LCA results and their representativeness. Results for the considered mobility scenarios have demonstrated the need to extend the scope of the materiality assessment, generally used for determining subcategories’ social significance from a single stakeholder perspective, by involving other stakeholders into the prioritization stage. Moreover, the proposed comprehensive S-LCA framework integrating the participatory approach is general enough to be applied to other product systems and sectors.

The conversion of agricultural waste materials such as bark or straw into 2nd generation biofuels constitutes an auspicious way to meet part of the future fuel demand in a sustainable way. The number of possible production routes is diverse, and the techno-economic analyses of these routes have been conducted in very different ways. The route involving gasification, gas purification, and a subsequent Fischer-Tropsch synthesis enables the production of hydrocarbons that achieve current fuel standards after upgrading in the existing refinery infrastructure. To evaluate a promising biomass-to-liquid process, a methodology is presented that incorporates economic constraints into the process design, allowing identification of a regionally optimal process design. The production costs of the new concepts are estimated by setting up a detailed flowsheet simulation in AspenPlus® based on experimental data from the successful demonstration runs of the EU-Project COMSYN. In addition, an existing techno-economic evaluation methodology incorporated into the in-house software tool TEPET (Techno-Economic Process Evaluation Tool) has been extended to evaluate the processes’ performance in different European regions and to include transportation and refining costs. The approach enables the identification of regional sweet spots shown on a map for Central-Europe, indicating production costs and favorable process design for each region. Furthermore, the results of an automated mapping for the optimal process design depending on the heat and electricity market are presented. The performed analyses show that the techno-economic evaluation tends to expand the technology in regions with low feedstock costs, while the optimal process design is defined by the regional heat and electricity market. In this work, net production costs of less than 1.12 €2019/lbiofuel were determined for regions in Hungary, Poland and Slovakia.

The rapid growth of agriculture has led to a significant increase in energy utilization and CO2 emissions. Agriculture performs a pivotal role in improving every country’s economy. The current study has the main objective to analyses the long-run impact of agricultural CO2 discharge on economic growth, energy consumption (electricity utilization in the agriculture sector), financial development, foreign direct investment (FDI), and population for India. For the period 1978–2018, we applied ADF, PP, ERS, and KPSS unit root and Z&A and CMR structural interval tests to evaluate the stability and breaks in the data set. We check the cointegration of study variables through ARDL, Engle–Granger, and Johansen’s cointegration approaches. The findings of the long-run analysis showed a cointegration among the variables, which reveals that an escalation in economic growth and financial development refine the natural environment, while the upsurge in FDI and population further deteriorate the climate in India. However, agricultural sector electricity use shows an insignificant association with CO2 emissions for both periods. On the basis of results, we recommend that legislators should offer an environment that provides opportunities for financial development and economic growth.