Carbon Productivity and Mitigation: Evidence from Industrial Development and Urbanization in the Central and Western Regions of China

This paper investigates carbon productivity (CP) from the perspectives of industrial development and urbanization to mitigate carbon emissions. We propose a hybrid model that includes a spatial lag model (SLM) and a fixed regional panel model using data from the 17 provinces in the central and western regions of China from 2000 to 2018. The results show that the slowly increasing CP has significant spatial spillover effects, with High–High (H–H) and Low–Low (L–L) spatial distributions in the central and western regions of China. In addition, industrial development and urbanization in the study area play different roles in CP, while economic urbanization and industrial fixed investment negatively affect CP, and population urbanization affects CP along a U-shape curve. Importantly, the results show that the patterns of industrial development and urbanization that influence CP are homogenous and mutually imitated in the 17 studied provinces. Furthermore, disparities in CP between regions are due to industrial workforce allocation (TL), but TL has been inefficient; industrial structure upgrades are slowly improving conditions. Therefore, the findings suggest that, in the short term, policymakers in China should implement industrial development policies that reduce carbon emissions in the western and central regions by focusing on improving industrial workforce allocation.

Sustainable Allocation Model of Construction Workforce for Work Resumption during COVID-19

COVID-19 has posed challenges for the construction industry, such as precise pandemic control, sustainable labor relations, and loss minimization. In response to these challenges, this study has developed a decision model that optimizes workforce allocation for projects to achieve sustainable workforce management, a tradeoff between pandemic prevention and work resumption. The priority of project resumption was evaluated using basic characteristics, the long- and short-term strategies, and the regional pandemic situation. The energy level of skilled workers was graded according to construction team size, skill level, and experience. Sustainable allocation principles and paths were explored to target four different types of work resumption plans. We used the cellular automaton (CA) technique to simulate the sustainable allocation model. We also analyzed the similarity function of energy levels and the time-cost function of allocation. The case study of the SGJ Construction demonstrates that this allocation model can accurately simulate work resumption and provide a sustainable allocation decisions and tools under pandemic. Also, it implies balanced interests and concerns between construction companies and the society for work resumption during COVID-19.

Cost-efficient pre-commercial thinning: effects of method and season of early cleaning

This study’s aim was to identify how the application season and the method of early cleaning (EC), the first stage of multistage pre-commercial thinning (PCT), affected the time consumption in EC and in the subsequent second PCT operation. The worktime in EC was recorded in the spring, summer, and autumn in 22 sites, which were either totally cleaned or point cleaned. Later, these sites were measured at the time of the second PCT. Time consumption was estimated in PCT, based on the removal of the sites. The time consumption in EC was 5.3 productive work hours (pwh) ha, 7.3 pwh ha, and 6.2 pwh ha respectively in the spring, summer, and autumn. EC in the spring instead of the summer saved 27–30% of working time, depending on the cleaning method. Point cleaning was 0.8 pwh ha quicker than total cleaning, but the difference was statistically insignificant. The second stage, PCT, was 1 pwh ha slower to conduct in sites which had been early cleaned in the spring instead of the summer. However, at the entire management program level, EC applied in the spring or autumn instead of the summer saved 11% or 5% respectively of the total discounted costs (3% interest rate) of multistage pre-commercial thinning. Today, the commonest time to conduct EC is in the summer, which was the most expensive of the analyzed management alternatives here. We can expect savings in juvenile stand management in forestry throughout boreal conifer forests by rethinking the seasonal workforce allocation.–1–1–1–1–1

Maximising profitability of quaternary sector organisations through workforce optimisation

PurposeThe purpose of this paper is to develop a workforce optimisation model that maximises the profitability of a knowledge-based service organisation in the quaternary sector.Design/methodology/approachAn optimisation model that allocates resources from different skillsets and seniority to projects that are delivered from several geographies has been developed in this paper. With the objective of maximising the profitability of a pipeline of projects, the model selects which projects to accept and which not to and indicates how many resources to hire for (or layoff from) each skillset-seniority-geography combination.FindingsThe paper discusses the model and its scalable nature. Through hypothetical scenarios, it is shown that the model, using a simple non-linear algorithm, converges to optimal solutions.Research limitations/implicationsThe model depends on inputs that are exogenously supplied by the organisation. The applicability of the outcome is dependent on them. However, on the other hand, it allows for the alignment of the outcomes with the strategic objective of the organisation.Practical implicationsThe paper discusses the multi-dimensional nature of effective human resource allocation problem. It not only maximises profitability but also allows organisations to strategically screen projects. With proper calibration and minor modifications, the model may be used to allocate resources across the knowledge-based industry.Originality/valueThe paper integrates the demand and supply-side problems of workforce allocation to projects in a novel way to form a tractable model that is pragmatic and applicable.

Pursuing effectiveness and efficiency: Data model for workforce scheduling at Alvarino

This case involves actual data obtained from an interview with a partner, a manager and two associates at a firm located in Singapore, Alvarino (pseudonym). The firm is part of a global network of accountancy and business advisory firms. The network comprises of more than 100 independently owned and managed firms that straddle more than 100 geographical locations across the world. The case illustrates issues that Alvarino experienced in scheduling their staff for audit advisory engagement. As a service oriented and cost-conscious business, workforce scheduling is essential to help Alvarino’s management optimise its workforce allocation. The objective of this case is to create a data model that maps user and data requirements to optimise Alvarino’s workforce-scheduling processes.