Many European nations, including Spain, are having difficulties achieving their Sustainable Development Goals (SDGs) objectives. Resultantly, the current research discusses the development of an SDG framework for Spain, which can be utilized as a model for other EU nations. This research examines whether GDP growth, technological innovation, and energy sources (renewable and non-renewable) have any effect on CO₂ emissions utilizing data from 1980 to 2018. The study applied the non-linear ARDL (NARDL) to investigate these interrelations. The outcomes from the NARDL revealed that positive (negative) shocks in renewable energy improve environmental quality in Spain. Furthermore, positive (negative) shocks in technological innovation lead to a decrease (increase) in CO₂. Lastly, positive (negative) shocks in energy consumption lead to an increase (decrease) in CO_2. The results reveal that Spain is on the right path towards decarburization. This research has important policy ramifications for the policymakers and government of Spain as well as neighbouring countries. GRAPHICAL ABSTRACT

In this paper, we adopt the bibliometric analysis software CiteSpace to analyze the research status quo and evolution trend of pickup and delivery problem (PDP), an important real-world issue occurring in logistics and transportation. We obtain 819 documents with the topic of PDP that were published in the Web of Science core collection during the period 1995–2021, and acquire their basic situation of posting trend and category distribution. Next, we employ CiteSpace to draw scientific knowledge maps and perform the corresponding visualization analysis, which mainly include the following aspects: (a) collaboration analysis of author, country, and institution; (b) co-citation analysis of author, journal, and reference; (c) citation burst detection of keyword; (d) co-citation clustering analysis of reference. The results show that PDP research has gradually become interdisciplinary and highly comprehensive, and the evolution trend of hot topics also reflects that the research directions involve multiple academic disciplines and professional areas ranging from algorithm design to logistics management. The changing knowledge components reveal the fact that the development of PDP research is highly related to the diversity and uncertainty of realistic logistics industry contexts. Study in this paper provides comprehensive understandings of PDP research for scholars and logistics practitioners, inspiring its further investigation.

The Chinese national government and local governments have introduced multiple incentive measures to increase the market share of new energy vehicles (NEVs), such as dual credit policy, financial subsidies, and building new charging infrastructures. However, the government's budget to support the development of NEVs is limited. In this paper, we consider a duopolistic market consisting of a gasoline vehicle (GV) firm and an electric vehicle (EV) firm and develop a multi-level game-theoretic model based on the fact that the Chinese government seeks to achieve a given market share target with the minimum expenditure. A comparison of the equilibrium results in three incentive schemes differentiated by the financial subsidy is conducted to investigate the effectiveness of various incentive schemes. Furthermore, we consider a real situation in China that the government attempts to achieve a target for the total number of charging infrastructures through a reasonable policy design. The results in this study reveal that, with the EV market share target in mind, raising the requirements of dual credit policy has no effect on the EV firm's profit under EV purchase subsidy, is beneficial to the EV firm's profit under construction cost sharing subsidy, and is detrimental to the EV firm's profit under per-unit construction subsidy. It is worthwhile for the government to subsidize for infrastructure construction effort rather than consumers' purchase cost. Given a target for the total number of charging infrastructures, construction cost sharing subsidy can provide more motivation for the EV firm to build charging infrastructures than per-unit construction subsidy.

In this paper, we employ a target-oriented approach to analyze the multi-attribute route choice decision of travelers in the stochastic tolled traffic network, considering the influence of three attributes, which are (stochastic) travel time, (stochastic) late arrival penalty, and (deterministic) travel cost. We introduce a target-oriented multi-attribute travel utility model for this analysis, where each attribute is assigned a target by travelers, and travelers’ objective is to maximize their travel utility that is determined by the achieved targets. Moreover, the interaction between targets is interpreted as complementarity relationship between them, which can further affect their travel utility. In addition, based on this travel utility model, a target-oriented multi-attribute user equilibrium model is proposed, which is formulated as a variational inequality problem and solved with the method of successive average. Target for travel time is determined via travelers’ on-time arrival probability, while targets for late arrival penalty and travel cost are given exogenously. Lastly, we apply the proposed model on the Braess and Nguyen–Dupuis traffic networks, and conduct sensitivity analysis of the parameters, including these three targets and the target interaction between them. The study in this paper can provide a new perspective for travelers’ multi-attribute route choice decision, which can further show some implications for the policy design.