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The purpose of the paper is to identify the main determinants of the development of the neighbourhood electric vehicle (NEV) industry in China, including influences from private stakeholders as well as the government, and domestic as well as foreign interest groups. Particular emphasis is placed on the role of state‐owned enterprises (SOEs) and the relations between the government and the SOE sector.

The paper follows an inductive approach, and is largely based on interviews with industry actors and representatives for relevant government agencies, complemented with secondary data.

The preliminary findings suggest that unlike Western market economies, where the impact of public policy on innovation is relatively transparent and well recorded (in the form of fiscal and financial incentives and formal legislation), much of Chinese innovation is driven by less formal decisions taken in the nexus between SOEs and relevant state agencies. Hence, although China suffers no lack of legislation at various levels (including laws, decrees, and guidelines) it is often difficult to identify the specific drivers for change.

The findings are useful for understanding the development of the NEV industry in China.

The current paper is the first application of the GIST (Governance of Innovation towards Sustainability Technology) framework to the case of the Chinese NEV industry.

This paper aims to explore the functionality of long tail markets (LTM), where the consumers cannot be reached or are ignored by the traditional mainstream businesses, in new products and business development.

First, the authors review two Chinese entrepreneurial practices in the Fintech sector and low-speed electric vehicles (LSEV) and describe their stylized facts; second, they explore a possible theoretical LTM framework to underscore these practices; third, they make a connection between LTM and existing business models and analyze its significance and practical implications in business, in particular, in developing economies.

The LTM business approach has helped Chinese companies in the Fintech sector and LSEVs gain global attention. The success factors of LTM for businesses are identifying a specific customer base, being aware of localization products and playing skillfully with regulations; the LTM approach has several overlaps with existing studies on niche products and base of the pyramid market.

Based on some emerging and attractive business practices in China, this paper offers a valuable attempt to theorize them as long tail phenomenon. The LTM thesis provides a potential framework to reference for similar methods elsewhere and may illuminate entrepreneurship to be explored in similar markets.

Recently, electric vehicles have been widely used in the cold chain logistics sector to reduce the effects of excessive energy consumption and to support environmental friendliness. Considering the limited battery capacity of electric vehicles, it is vital to optimize battery charging during the distribution process.

This study establishes an electric vehicle routing model for cold chain logistics with charging stations, which will integrate multiple distribution centers to achieve sustainable logistics. The suggested optimization model aimed at minimizing the overall cost of cold chain logistics, which incorporates fixed, damage, refrigeration, penalty, queuing, energy and carbon emission costs. In addition, the proposed model takes into accounts factors such as time-varying speed, time-varying electricity price, energy consumption and queuing at the charging station. In the proposed model, a hybrid crow search algorithm (CSA), which combines opposition-based learning (OBL) and taboo search (TS), is developed for optimization purposes. To evaluate the model, algorithms and model experiments are conducted based on a real case in Chongqing, China.

The result of algorithm experiments illustrate that hybrid CSA is effective in terms of both solution quality and speed compared to genetic algorithm (GA) and particle swarm optimization (PSO). In addition, the model experiments highlight the benefits of joint distribution over individual distribution in reducing costs and carbon emissions.

The optimization model of cold chain logistics routes based on electric vehicles provides a reference for managers to develop distribution plans, which contributes to the development of sustainable logistics.

In prior studies, many scholars have conducted related research on the subject of cold chain logistics vehicle routing problems and electric vehicle routing problems separately, but few have merged the above two subjects. In response, this study innovatively designs an electric vehicle routing model for cold chain logistics with consideration of time-varying speeds, time-varying electricity prices, energy consumption and queues at charging stations to make it consistent with the real world.

Motivated by a problem in the context of DiDi Travel, the biggest taxi hailing platform in China, the purpose of this paper is to propose a novel facility location problem, specifically, the single source capacitated facility location problem with regional demand and time constraints, to help improve overall transportation efficiency and cost.

This study develops a mathematical programming model, considering regional demand and time constraints. A novel two-stage neighborhood search heuristic algorithm is proposed and applied to solve instances based on data sets published by DiDi Travel.

The results of this study show that the model is adequate since new characteristics of demand can be deduced from large vehicle trajectory data sets. The proposed algorithm is effective and efficient on small and medium as well as large instances. The research also solves and presents a real instance in the urban area of Chengdu, China, with up to 30 facilities and demand deduced from 16m taxi trajectory data records covering around 16,000 drivers.

This study examines an offline and single-period case of the problem. It does not consider multi-period or online cases with uncertainties, where decision makers need to dynamically remove out-of-service stations and add other stations to the selected group.

Prior studies have been quite limited. They have not yet considered demand in the form of vehicle trajectory data in facility location problems. This study takes into account new characteristics of demand, regional and time constrained, and proposes a new variant and its solution approach.

This paper aims to investigate the influence of composite solder joint preparation on the thermal properties of metal-oxide-semiconductor field-effect transistors (MOSFETs) and the mechanical strength of the soldered joint.

Reinforced composite solder joints with the addition of titanium oxide nanopowder (TiO₂) were prepared. The reference alloy was Sn99Ag0.3Cu0.7. Reinforced joints differed in the weight percentage of TiO₂, ranging from 0.125 to 1.0 Wt.%. Two types of components were used for the tests. The resistor in the 0805 package was used for mechanical strength tests, where the component was soldered to the FR4 substrate. For thermal parameters measurements, a power element MOSFET in a TO-263 package was used, which was soldered to a metal core printed circuit board (PCB) substrate. Components were soldered in batch IR oven.

Shear tests showed that the addition of titanium oxide does not significantly increase the resistance of the solder joint to mechanical damage. Titanium oxide addition was shown to not considerably influence the soldered joint’s mechanical strength compared to reference samples when soldered in batch ovens. Thermal resistance R_thj-a of MOSFETs depends on TiO₂ concentration in the composite solder joint reaching the minimum R_thj at 0.25 Wt.% of TiO₂.

Mechanical strength: TiO₂ reinforcement shows minimal impact on mechanical strength, suggesting altered liquidus temperature and microstructure, requiring further investigation. Thermal performance: thermal parameters vary with TiO₂ concentration, with optimal performance at 0.25 Wt.%. Experimental validation is crucial for practical application. Experimental confirmation: validation of optimal concentrations is essential for accurate assessment and real-world application. Soldering method influence: batch oven soldering may affect mechanical strength, necessitating exploration of alternative methods. Thermal vs mechanical enhancement: while TiO₂ does not notably enhance mechanical strength, it improves thermal properties, highlighting the need for balanced design in power semiconductor assembly.

Incorporating TiO₂ enhances thermal properties in power semiconductor assembly. Optimal concentration balancing thermal performance and mechanical strength must be determined experimentally. Batch oven soldering may influence mechanical strength, requiring evaluation of alternative techniques. TiO₂ composite solder joints offer promise in power electronics for efficient heat dissipation. Microstructural analysis can optimize solder joint design and performance. Rigorous quality control during soldering ensures consistent thermal performance and mitigates negative effects on mechanical strength.

The integration of TiO₂ reinforcement in solder joints impacts thermal properties crucial for power semiconductor assembly. However, its influence on mechanical strength is limited, potentially affecting product reliability. Understanding these effects necessitates collaborative efforts between researchers and industry stakeholders to develop robust soldering techniques. Ensuring optimal TiO₂ concentration through experimental validation is essential to maintain product integrity and safety standards. Additionally, dissemination of research findings and best practices can empower manufacturers to make informed decisions, fostering innovation and sustainability in electronic manufacturing processes. Ultimately, addressing these social implications promotes technological advancement while prioritizing consumer trust and product quality in the electronics industry.

The research shows the importance of the soldering technology used to assemble MOSFET devices.

In this chapter, we draw on social science theoretical and empirical literatures to discuss the factors that influence buying and using an electric vehicle (EV), as well as how adopting an EV can impact on other travel choices or broader sustainability behaviours. We provide an overview of theories of technology adoption, which expose the interplay of individual, technological, and societal factors that dictate how rapidly a technology will spread throughout society. From the empirical literature, we show that far from being a purely economic or pragmatic decision, choosing an EV is also deeply grounded in social, moral and personality factors, such as self-presentation, norms and values, and appetite for risking the novel. Furthermore, since running an EV is not the same as running an internal combustion engine vehicle (ICEV), we explore how adopters adjust their behaviour to the technology, and also how EV ownership may trigger or undermine broader shifts in lifestyle required to achieve climate change and other sustainability goals. We therefore provide a critical reflection on the drivers, barriers, and behavioural implications of choosing an EV.

This chapter explores how technology availability and costs influence public opinion, vehicle ownership decisions, travel, and location choices. Attitudes towards electric vehicles (EVs) are considered within the broader context of other linked technological trends affecting automobility, with a particular focus on the shift to (electric powertrain) autonomous vehicles (AVs).

This chapter draws upon modelling of quantitative survey data from 1,426 Americans, which employed regression analysis to predict and understand variables linked to the preference for an AV over a human driver, percentage of trips taken by an AV, percentage of trips using dynamic ride-sharing (DRS) inside a shared autonomous vehicle (SAV), and factors affecting EV charging access in home and at work/school.

The findings show that full EV charging times significantly affect decisions for next household vehicle purchase. A lack of charging ability at home appears to be a significantly greater hindrance to respondents’ willingness to purchase full EVs than does a lack of charging ability at work. And home location choice impacts of AVs are not expected to be substantial. Considering future EV/AV ride-sharing (an important component of sustainable future mobility systems), DRS may ease congestion if SAV riders widely adopt DRS for work and school trips; however, sharing with strangers may not be popular in practice.

This chapter is useful to manufacturers and fleet operators for pricing and marketing decisions, and public transit authorities/providers can benefit from understanding evolving travel choices and land use patterns to craft equitable policies, and model future transportation demand to help plan services and infrastructure projects.