Green Facility Location

Facility location methods play a crucial role in specifying the optimum location options for various types of facilities. A question that arises is what makes a facility location decision a sustainable one? Facility location, also known as location analysis, is a known concept in the literature, but sustainable facility location is not. This requires appropriately defining the concept and framing the problem in order to address the relevant issues. Facility location models in the existing literature do not effectively include all the requirements of sustainable development. This paper serves as a discussion of the current literature concerning the sustainability aspects of the location problem. The aim is to conduct a comprehensive literature review to identify the characteristics of the sustainable facility location problem and propose a framework for classification of sustainability characteristics. The study shows that the location literature has steadily progressed toward considering not only economic but also social and environmental criteria in location decisions; but that many steps remain to be taken toward developing location models that integrate all three aspects of sustainability into decision making. The main motivation for the current study is to provide a foundation from which issues of sustainable development can be built into facility location and siting models.

As a major part of global development governance, G20 countries account for 80% of global carbon emissions. In order to achieve the goal of "carbon neutrality" proposed by the United Nations, it is important to compare and analyze the drivers of carbon emissions in G20 countries and provide recommendations for their carbon emission reduction. Based on the data of 17 G20 countries in the EORA database, this paper compares the drivers of carbon emissions of each country from 1990-2021 using weighted average structural decomposition and K-mean model. This paper focuses on four drivers, including carbon emission intensity, final demand structure, export structure, and production structure. Carbon emission intensity and final demand structure are the main factors of carbon emission reduction, and the other two factors have little influence. Among the G20 countries, the UK is in the first category because it does the best job on the four factors of carbon emissions, yet Italy is in the last category because it does not take full advantage of the four factors. Therefore, improving supply energy efficiency and adjusting demand, export, and industrial structure have become important tools for countries to transform and achieve carbon neutrality.

The rapid development of the global transportation industry has led to increased carbon dioxide emissions, intensifying the pressure to reduce these emissions. On the basis of constructing a global carbon emission analysis framework for the transportation industry, this study used panel data on carbon emissions from the transportation industry in 140 countries or regions for a long-term time series from 1971 to 2018. The standard deviation ellipse, Gini coefficient, and Moran’s I index were used to characterize the spatial patterns of carbon emissions in the global transportation industry. The factors influencing carbon emissions from the global transportation industry were analyzed using quantile regression. The main findings are as follows: (1) From the distribution pattern, the total carbon emissions from the global transportation industry showed a significant upward trend, and the spatial polarization characteristics were particularly significant. (2) The Gini coefficient of global carbon emissions from the transportation industry showed a significant downward trend, characterizing a more balanced spatial distribution. (3) From the perspective of correlation patterns, the spatial distribution of carbon emissions from the global transportation industry was positively correlated. (4) Regarding influencing factors, population size had a significant role in promoting carbon emissions from the transportation industry, and the difference was not apparent. The influence of affluence on carbon emissions was basically in line with the characteristics of the Kuznets curve, technological advances had a significant negative influence on carbon emissions, and participation in the global value chain had a significant influence on carbon emissions from countries or regions with high carbon emissions. In conclusion, it is necessary to enhance international cooperation on carbon emission management in the global transportation industry and adopt differentiated policy measures. For instance, we should accelerate the construction of a multimodal transport system, increase the promotion and support for new energy heavy-duty trucks, implement policies such as priority road rights for new energy heavy-duty trucks and reduce tolls on expressways, and deepen the integration of transportation and energy.

Mapping carbon emissions of China's domestic air passenger transport: From individual cities to intercity networks

This paper investigates the status and trends of global carbon emissions, including total emissions, per capita emissions, and emission intensity. It reviews the evolution and influencing factors of international carbon emissions. The increase in emissions is mainly concentrated in emerging economies and developing countries. Developed nations, despite having high total emissions, exhibit low emission intensity and stable or declining trends, often aiming for “carbon neutrality.” Emerging economies, including newly industrialized countries, are experiencing rapid increases in emissions, and are setting targets for carbon peaking and neutrality. Additionally, this paper examines global carbon emission policies, summarizing strategies and commitments for emission reduction. In the end, it anticipates future policy directions for global carbon emissions.

Decomposition analysis of energy-related carbon emissions from the transportation sector in Beijing

3D coverage location modeling of Wi-Fi access point placement in indoor environment

In this chapter, we consider models for facility sizing and location under oligopolistic market equilibrium conditions on a spatial network. As in Chapter 6, we employ the paradigm of a large firm locating facilities on a defined network. To solve facility sizing/location models in oligopolistic competition, sensitivity based bilevel algorithms are developed. We will observe that there exist both significant similarities and differences between the algorithms developed in this chapter and those presented in Chapter 6 where a spatial price equilibrium characterized the economic conditions under which the firm of interest made its locational decisions.

This paper presents a new concept of partial coverage distance, where demand points within a given threshold distance of a new facility are covered in the traditional sense, while non-covered demand points are penalized an amount proportional to their distance to the covered region. Two single facility location models, based on the minisum and minimax criteria, are formulated with the new distance function, and the structure of the models is analysed.

Quantifying the climate impact of emissions from land-based transport in Germany

Examining the driving factors of energy related carbon emissions using the extended STIRPAT model based on IPAT identity in Xinjiang

<p>Investigating the spatiotemporal characteristics and factors of urban carbon emissions is essential to reduce carbon emissions and achieve dual carbon goals. In this study, we examined the change tendency of carbon emissions using the coefficient of variation, the Sen’s slope method, and the Mann–Kendall (MK) test and explored the effects of socioeconomic and environmental variables on carbon emissions using GeoDetector, in Qingdao City. The results revealed that: (1) From 2000 to 2020, carbon emissions increased annually, the area ratio of high carbon emissions increased and of low carbon emissions decreased yearly. Over 73% of carbon emissions have changed in a moderate way (0.1< CV < 1) and 80% of Qingdao City experienced an increased tendency (β > 0) in carbon emissions. (2) Carbon emissions diminished gradually from the urban center to the periphery. There were significant spatiotemporal disparities from one another in the subareas, Municipal districts had the largest variation degree (CV=0.32) and a huge growth trend of carbon emissions, while Laixi, Jiaozhou, and Pingdu were minor. (3) Socio-economic factors demonstrated a stronger ability to explain carbon emissions than environmental factors. GDP density, population density and floor area ratio were the key variables that affect the spatial distribution of carbon emissions, and the interaction between GDPD and PD can explain 81.9% of the carbon emissions in Qingdao. New technologies and materials, low-carbon energy consumption and lifestyles, and acceptable economic growth were the main strategies for Qingdao to become a low-carbon city.</p>

The marine fishing industry has a huge carbon sink potential and is also an important source of carbon emissions. The low-carbon development of the marine fishing industry is particularly important. Based on the perspective of carbon neutrality, this study analyzed the trend of net carbon emissions, carbon emissions and carbon sinks in the offshore fishing industry in China and 11 coastal provinces from 2010 to 2019 and decomposed the driving factors of the net carbon emissions of the offshore fishing industry with the LMDI decomposition method. The results show the following: (1) China’s offshore fishing industry is in a partially carbon-neutral state. Overall, the net carbon emissions have decreased, and the carbon neutrality capacity has improved. However, the net carbon emissions have increased since 2016. From 2010 to 2019, both the carbon emissions and carbon sinks of China’s offshore fishing industry declined. Carbon emissions fluctuated at first and then declined rapidly, while carbon sinks rose slowly and then showed a significant downward trend. (2) The offshore fishing industry in coastal provinces is also in a state of partial carbon neutrality, and the trends of carbon emissions, carbon sinks and net carbon emissions in most provinces are consistent with the national trends, but there are large differences between regions. (3) For the whole country, among the driving factors of net carbon emissions in the offshore fishing industry, industrial development is the main positive driving factor, and population size is the main negative driving factor. The net carbon coefficient and energy intensity also play a certain role in driving net carbon emissions. (4) Population size is an important inhibitory factor for the net carbon emissions of the offshore fishing industry in most coastal provinces, and the driving direction of the net carbon coefficient, energy intensity and industrial development is inconsistent. Based on the above research, relevant suggestions are put forward for the green development of the marine fishing industry.

Distributionally robust facility location with bimodal random demand

Classical facility location models can generate solutions that do not maintain consistency in the set of utilized facilities as the number of utilized facilities is varied. We introduce the concept of nested facility locations, in which the solution utilizing p facilities is a subset of the solution utilizing q facilities, for all i ≤ p < q ≤ j, given some lower limit i and upper limit j on r, the number of facilities that will be utilized in the future. This approach is demonstrated with application to the p-median model, with computational testing showing these new models achieve reductions in both average regret and worst-case regret when r ≠ p facilities are actually utilized.