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Mitigating agricultural greenhouse gas (GHG) emissions is an essential part of China's effort to achieve net-zero emissions. This study assesses the cost-effectiveness of China's agricultural GHG reduction under diverse carbon policies.

The study employs a parametric non-radial distance function approach and estimates the technical abatement potential and marginal abatement cost (MAC) of GHG in China's agricultural sector for the 2008–2017 period.

Agriculture is expected to make a great contribution to China's net-zero emissions progress. This study empirically analyses the cost-effectiveness of China's agricultural GHG reduction under diverse carbon policies. A parametric non-radial distance function approach is used to derive technical abatement potential and MAC of GHG for the 2008–2017 period. The results indicate that no significant improvement had been achieved in terms of agricultural GHG reduction in China during 2008–2017. The country's agricultural sector could reduce 20–40% GHG emissions with a mean value of 31%. In general, western provinces have larger reduction potential than eastern ones. The average MAC for the whole country is 4,656 yuan/ton CO₂e during 2008–2017. For most western provinces, their MAC values are considerably higher than those for most eastern provinces. Compared with previous sectoral estimates of GHG mitigation cost, this study’s estimates indicate that reducing agricultural GHG emissions in some provinces is likely to be cost-effective. The Chinese government should consider expanding its national carbon market to cover agricultural sector.

The Chinese government should consider expanding its national carbon market to cover agricultural sector.

Existing studies in the field mostly ignore input constraints, which is inconsistent with carbon mitigation policy practice, especially in the agricultural sector. This study’s approach integrates both input and output constraints reflecting differing policy practice.

Population growth, urbanisation and the increased use of online shopping are some of the key challenges affecting the traditional logistics model. The purpose of this paper is to focus on the distribution of grocery products ordered online and the subsequent home delivery and click and collect services offered by online retailers to fulfil these orders. These services are unsustainable due to increased operational costs, carbon emissions, traffic and noise. The main objective of the research is to propose sustainable logistics models to reduce economic, environmental and social costs whilst maintaining service levels.

The authors have a mixed methodology based on simulation and mathematical modelling to evaluate the proposed shared logistics model using: primary data from a major UK retailer, secondary data from online retailers and primary data from a consumer survey on preferences for receiving groceries purchased online. Integration of these three data sets serves as input to vehicle routing models that reveal the benefits from collaboration by solving individual distribution problems of two retailers first, followed by the joint distribution problem under single decision maker assumption.

The benefits from collaboration could be more than 10 per cent in the distance travelled and 16 per cent in the time required to deliver the orders when two online grocery retailers collaborate in distribution activities.

The collaborative model developed for the online grocery market incentivises retailers to switch from current unsustainable logistics models to the proposed collaborative models.

We explore how cultural factors at both socio-economic and psychological individual levels affect the present generation's beneficence toward future generations in organizations and society. We examine how socio-economic mechanisms may influence the present generation's focus on the future consequences of their decisions. In addition, we examine how self-construals in different cultures might result in different mechanisms underlying the reduction of psychological distance between generations in different cultures. Implications of our cross-cultural analysis to intergenerational decision making within the context of group research in general are discussed.

Liquefied petroleum gas (LPG) transportation risks depend on aspects such as the total length of the trip and population density along the route. Choosing to deliver the product on non-busy days and reducing distances travelled may help to reduce these risks and lower the level of air pollution generated by the transportation trucks. The purpose of this paper is to reduce LPG delivery impact.

A three-stage methodology is proposed. First, rules are created in order to choose which clients have to be visited each day to avoid deliveries in downtown areas during business days. Second, an Iterated Local Search (ILS) metaheuristic is proposed for the capacitated p-median problem to group the chosen customers. Finally, another ILS is proposed to solve the Travelling Salesman Problem, for each truck to follow a better route while visiting its customers.

The methodology resulted in a 24.8 per cent reduction in distances travelled, representing an annual reduction of 32,716 kg in CO₂ emissions. The average amount of product sold per kilometre travelled improved by 72 per cent.

The literature shows a clear need for companies to consider sustainability in their daily decisions. However, especially in developing countries, there is a fear that protecting the environment may cost money. This main contribution of this paper is that it presents a real solution, serving as a guide for companies to improve their transportation system, resulting in environmental and economic benefits.

Notes buyer‐seller interdependence is crucial to industrial marketing — industrial firms establish buyer‐seller relationships of the close kind and long term. Examines buyer‐seller nature in industrial markets by considering development as a process through time, it is based on ideas from the IMP Project. Analyses the process of establishment and development of relationship over time by considering stages in revolution. Notes also that this process described herein does not argue the inevitability of relationship development. Discusses the pre‐relationship stage: the early stage; the development stage; the long‐term stage; and the final stage with points to debate. Describes how the development of buyer‐seller relationships can be seen as a process in terms of: the increasing experience of the two companies; reduction in their uncertainty and the distance between them; growth of both actual and perceived commitment; formal and informal adaptation to each other and the investments and savings involved. Finally, states it is important to emphasize that companies should examine existing relationships according to the potential and stage of development.

On 24 January 2020, France informed WHO of three cases of novel coronavirus, all of whom had travelled from Wuhan, China. These three cases were the first confirmed cases in Europe. By 13 March 2020, Europe had become the epicentre of the pandemic with more reported cases and deaths than the rest of the world combined, apart from the People’s Republic of China. Many European countries like Italy, France and Germany took drastic actions and subsequently announced a lockdown, while other countries like the UK, the Netherlands and Sweden were much more hesitant to introduce such far-reaching safety measures. This chapter provides a literature overview of how the variation in such measures in Europe has ultimately resulted in changes in daily activities and travel behaviour during the pandemic. It focusses on five main themes: (i) reduction in mobility and activities, (ii) spatial-temporal adjustments in out-of-home activities in which people still participated, (iii) modal adjustments especially among people who used to travel by public transport before the pandemic, (iv) new out-of-home activities including new outdoor activities and (v) digital adaptations as several out-of-home activities were replaced by digital activities, with special attention to the experience of teleworking.

This study aims to understand the influence of megasonic (MS)-assisted agitation on printed circuit boards (PCBs) electroplated using copper (Cu) electrolyte solutions to improve plating efficiencies through enhanced ion transportation.

The impact of MS-assisted agitation on topographical properties of the electroplated surfaces was studied through a design of experiments by measuring surface roughness, which is characterised by values of the parameter Ra as measured by white light phase shifting interferometry and high-resolution scanning electron microscopy.

An increase in Ra from 400 to 760 nm after plating was recorded for an increase in acoustic power from 45 to 450 W. Roughening increased because of micro-bubble cavitation energy and was supported through direct imaging of the cavitation. Current thieving effect by the MS transducer induced low currents, leading to large Cu grain frosting and reduction in the board quality. Current thieving was negated in plating trials through specific placement of transducer. Wavy electroplated surfaces, due to surface acoustic waves, were also observed to reduce the uniformity of the deposit.

The formation of unstable transient cavitation and variation of the topology of the Cu surface are unwanted phenomena. Further plating studies using MS agitation are needed, along with fundamental simulations, to determine how the effects can be reduced or prevented.

This study can help identify manufacturing settings required for high-quality MS-assisted plating and promote areas for further investigation, leading to the development of an MS plating manufacturing technique.

This study quantifies the topographical changes to a PCB surface in response to MS agitation and evidence for deposited Cu artefacts due to acoustic effects.

Socioeconomic status (SES) has been known to be associated with many aspects of social life such as health. We argue that social stratification remains relevant in understanding differentials in health during a pandemic, as countries globally have encouraged or enforced social distancing and remote work measures.

By examining data sources and news reports on the COVID-19 pandemic, we aim to highlight the relationship between SES and morbidity, through the ability to adopt social distancing measures and work remotely. Utilizing publicly available data from the Maryland Transport Institute and the US Census, we performed linear regressions on median income, social distancing index and percentage of individuals working from home.

Individuals with higher SES are more likely to have jobs that provide opportunities for remote work to be performed, which allows for social distancing. Comparatively, individuals with lower SES are more likely to be involved in jobs that cannot be performed remotely. The linear regression models suggest a positive moderate and significant correlation between median income and social distancing index (R² = 0.4981, p-value < 0.001), and a positive weak and significant correlation between median income and remote work (R² = 0.2460, p-value < 0.001).

Governments need to account for SES in policymaking to reduce inequalities in health.

The paper aims to improve the understanding of social stratification and morbidity through examining data on the COVID-19 pandemic.

The authors investigate the effect of weather and mobility on the spread of the Covid-19 pandemic.

The authors first estimate the effective reproduction number (Rt) as a proxy of the spread of the Covid-19 pandemic and then study the relationship between the latter and weather and mobility in a panel data framework. The authors use US daily infections data between February and September of 2020 at the county level.

The authors find that lower temperatures are associated with a higher Rt, and this effect is greater at temperatures below 0°C. In addition, mobility reductions related to certain types of locations (retail and recreation, transit stations and workplaces) are effective at reducing Rt, but it is an increase in the time spent in parks that most helps reduce the spread of the pandemic.

The estimates imply that a 20°C fall in temperature from summer to winter would increase Rt by +0.35, which can be the difference between a well-controlled evolution and explosive behavior of the spread of the virus. Applying these coefficients estimated with US county data to aggregate series from other countries helps explain the resurgence of the pandemic in the Northern Hemisphere during the winter of 2020. The results show that mobility reduction and social distance are best policies to cope with the Covid-19 outbreak. This strong policy lesson will help facing similar outbreaks in the future.

The purpose of this study is to model a vehicle routing problem with integrated picking and delivery under carbon cap and trade policy. This study also provides sensitivity analyses of carbon cap and price to the total cost.

A mixed integer linear programming (MILP) model is formulated to model the vehicle routing with integrated order picking and delivery constraints. The model is then solved by using the CPLEX solver. Carbon footprint is estimated by a fuel consumption function that is dependent on two factors, distance and vehicle speed. The model is analyzed by considering 10 suppliers and 20 customers. The distance and vehicle speed data are generated using simulation with random numbers.

Significant amount of carbon footprint can be reduced through the adoption of eco-efficient vehicle routing with a marginal increase in total transportation cost. Sensitivity analysis indicates that compared to carbon cap, carbon price has more influence on the total cost.

The model considers mid-sized problem instances. To analyze large size problems, heuristics and meta-heuristics may be used.

This study provides an analysis of carbon cap and price model that would assist practitioners and policymakers in formulating their policy in the context of carbon emissions.

This study provides two significant contributions to low carbon supply chain management. First, it provides a vehicle routing model under carbon cap and trade policy. Second, it provides a sensitivity analysis of carbon cap and price in the model.