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1 – 10 of over 1000Alina Steblyanskaya, Mingye Ai, Artem Denisov, Olga Efimova and Maksim Rybachuk
Understanding China's carbon dioxide (
Abstract
Purpose
Understanding China's carbon dioxide (
Design/methodology/approach
In this study using the input and output (IO) table's data for the selected years, the authors found the volume of
Findings
Results show that in the industries with a huge volume of
Originality/value
“Transport, storage, and postal services” and “Smelting and processing of metals” industries in China has the second place concerning emissions, but over the past period, emissions have been sufficiently reduced. “Construction” industry produces a lot of emissions, but this industry does not carry products characterized by large emissions from other industries. Authors can observe that Jiangsu produces a lot of
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The main goal of this paper is to examine the evolution of Latin American productive integration in terms of the regional value added incorporated in intra-regional exports of…
Abstract
Purpose
The main goal of this paper is to examine the evolution of Latin American productive integration in terms of the regional value added incorporated in intra-regional exports of Argentina, Brazil, Chile, Colombia, Mexico and Peru. In addition, the study traces the trade and productive integration trajectories for each of these countries from 1995 to 2015.
Design/methodology/approach
Based on the use of OECD’s global ICIO input-output tables, this paper applies the methodological framework by Wang et al. (2018) for the analysis of trade flows at the bilateral level, which allows breaking down the value of gross exports of each sector-country, depending on the origin of the value added contained in exports, as well as their use.
Findings
The estimates show very low shares of value added from regional partners in the intra-regional exports of the countries studied. Conversely, the weight of the value added incorporated in these exports by countries outside the region has increased in tandem with China’s expanding involvement in Latin America. This development, along with the downward trend in domestic value added incorporated in exports, indicates a lack of a regional integration process of any depth.
Originality/value
This article addresses an economic problem of conventional importance from a global value chain perspective using a novel methodology based on the use of global input–output tables.
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Kazhal Gharibi and Sohrab Abdollahzadeh
To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by…
Abstract
Purpose
To maximize the network total profit by calculating the difference between costs and revenue (first objective function). To maximize the positive impact on the environment by integrating GSCM factors in RL (second objective function). To calculate the efficiency of disassembly centers by SDEA method, which are selected as suppliers and maximize the total efficiency (third objective function). To evaluate the resources and total efficiency of the proposed model to facilitate the allocation resource process, to increase resource efficiency and to improve the efficiency of disassembly centers by Inverse DEA.
Design/methodology/approach
The design of a closed-loop logistics network for after-sales service for mobile phones and digital cameras has been developed by the mixed-integer linear programming method (MILP). Development of MILP method has been performed by simultaneously considering three main objectives including: total network profit, green supply chain factors (environmental sustainability) and maximizing the efficiency of disassembly centers. The proposed model of study is a six-level, multi-objective, single-period and multi-product that focuses on electrical waste. The efficiency of product return centers is calculated by SDEA method and the most efficient centers are selected.
Findings
The results of using the model in a case mining showed that, due to the use of green factors in network design, environmental pollution and undesirable disposal of some electronic waste were reduced. Also, with the reduction of waste disposal, valuable materials entered the market cycle and the network profit increased.
Originality/value
(1) Design a closed-loop reverse logistics network for after-sales services; (2) Introduce a multi-objective multi-echelon mixed integer linear programming model; (3) Sensitivity analysis use Inverse-DEA method to increase the efficiency of inefficient units; (4) Use the GSC factors and DEA method in reverse logistics network.
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Ray Sastri, Fanglin Li, Arbi Setiyawan and Anugerah Karta Monika
The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about…
Abstract
Purpose
The tourism multiplier effect (TME) is the total economic impact of tourism demand, representing the linkages between tourism and other businesses in an area. However, study about it is limited in Indonesia, especially at the provincial level and after the COVID-19 crisis. This study aims to estimate the TME in all provinces of Indonesia, test its differences in priority and non-priority areas before and after the COVID-19 crisis, analyze its spatial distribution and examine the determinant factor of TME
Design/methodology/approach
This study applies an input-output model to measure the TME of all provinces in Indonesia, an independent sample t-test to examine the similarity of TME in priority and nonpriority areas, a paired sample t-test to examine the similarity of it before and after the COVID-19 crisis, and spatial analysis to check its spatial relationship.
Findings
The result shows that regional TME ranges from 1.25 to 2.05 in 2019, which changed slightly over time. The empirical result shows the TME difference before and after the COVID-19 crisis, and there is a spatial correlation in terms of TME with the hot spots are clustered in the eastern region of Indonesia, However, there was a slight change in the position of hot spots during the COVID-19 crisis. Moreover, the spatial model shows that value-added and employment in agriculture, manufacturing, trade and transportation affect the size of TME.
Originality/value
This study contributes to the academic literature by providing the first estimate of the TME at the provincial level in Indonesia, comparing the it in priority and non-priority areas before and after the COVID-19 crisis, and mapping its spatial distribution.
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Pimtong Tavitiyaman, Tin-Sing Vincent Law, Yuk-Fai Ben Fong and Tommy K.C. Ng
This study aims to explore the influence of health-care service quality on customers’ perceived value, satisfaction, effectiveness and behavioural intention concerning district…
Abstract
Purpose
This study aims to explore the influence of health-care service quality on customers’ perceived value, satisfaction, effectiveness and behavioural intention concerning district health centres (DHCs) in Hong Kong. This research also intends to assess customers’ perception of the subsidy scheme and its influence on the relationships amongst the aforementioned constructs.
Design/methodology/approach
The convenience and snowball sampling approaches were adopted, and the self-administered questionnaire was sent to 309 customers of DHCs.
Findings
Service quality attributes in terms of staffing and procedures positively increased customers’ perceived value and staffing, procedures and operations. Physical facilities positively promoted customers’ satisfaction, consequently improving DHCs’ effectiveness and behavioural intention. However, core treatments and services of DHCs did not impact customers’ perceived value and satisfaction. Furthermore, customers receiving subsidies exhibited a more positive perception than those without subsidies.
Practical implications
Health-care organisations are advised to strategically allocate resources (staffing, facilities and procedures and operations management) to optimise overall performance outcomes. DHC operators could reinforce the core services of DHCs and health-care voucher subsidies to local citizens so as to enhance the effectiveness of DHCs and behavioural intention of customers.
Originality/value
This study integrates the input–process–output approach in measuring the effectiveness of and customers’ behavioural intention towards newly established DHCs.
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Bishal Dey Sarkar and Laxmi Gupta
The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and…
Abstract
Purpose
The conflict in Russian Ukraine is a problem for the world economy because it hinders growth and drives up inflation when it is already high. The trade route between India and Russia is also impacted by the Russia-Ukraine crisis. This study aims to compile the most recent data on how the present global economic crisis is affecting it, with particular emphasis on the Indian economy.
Design/methodology/approach
This research develops a mathematical forecasting model to evaluate how the Russia-Ukraine crisis would affect the Indian economy when perturbations are applied to the major transport sectors. Input-output modeling (I-O model) and interval programing (IP) are the two precise methods used in the model. The inoperability I-O model developed by Wassily Leontief examines how disruption in one sector of the economy spreads to the other. To capture data uncertainties, IP has been added to IIM.
Findings
This study uses the forecasted inoperability value to analyze how the sectors are interconnected. Economic loss is used to determine the lowest and highest priority sectors due to the Russia-Ukraine crisis on the Indian economy. Furthermore, this study provides a decision-support conclusion for studying the sectors under various scenarios.
Research limitations/implications
In future studies, other sectors could be added to study the Russian-Ukrainian crises’ effects on the Indian economy. Perturbation is only applied to transport sectors and could be applied to other sectors for studying the effects of the crisis. The availability of incomplete data is a significant concern in this study.
Originality/value
Russia-Ukraine conflict is a significant blow to the global economy and affects the global transportation network. This study discusses the application of the IIM-IP model to the Russia-Ukraine conflict. It also forecasts the values to examine how the crisis affected the Indian economy. This study uses a variety of scenarios to create a decision-support conclusion table that aids decision-makers in analyzing the Indian economy’s lowest and most affected sectors as a result of the crisis.
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Tiago Ferreira Barcelos and Kaio Glauber Vital Costa
This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000…
Abstract
Purpose
This study aims to analyze and compare the relationship between international trade in global value chains (GVC) and greenhouse gas (GHG) emissions for Brazil and China from 2000 to 2016.
Design/methodology/approach
The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.
Findings
The data reveals that Chinese production and consumption emissions are associated with power generation and energy-intensive industries, a significant concern among national and international policymakers. For Brazil, the largest territorial emissions captured by the metrics come from services and traditional industry, which reveals room for improving energy efficiency. The analysis sought to emphasize how the productive structure and dynamics of international trade have repercussions on the environmental dimension, to promote arguments that guide the execution of a more sustainable, productive and commercial development strategy and offer inputs to advance discussions on the attribution of climate responsibility.
Research limitations/implications
The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.
Originality/value
Comparative analysis of emissions embodied in traditional sectoral trade flows and GVC, on backward and forward sides, for developing countries with the main economic regions of the world.
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Camille J. Mora, Arunima Malik, Sruthi Shanmuga and Baljit Sidhu
Businesses are increasingly vulnerable and exposed to physical climate change risks, which can cascade through local, national and international supply chains. Currently, few…
Abstract
Purpose
Businesses are increasingly vulnerable and exposed to physical climate change risks, which can cascade through local, national and international supply chains. Currently, few methodologies can capture how physical risks impact businesses via the supply chains, yet outside the business literature, methodologies such as sustainability assessments can assess cascading impacts.
Design/methodology/approach
Adopting a scoping review framework by Arksey and O'Malley (2005) and the PRISMA extension for scoping reviews (PRISMA-ScR), this paper reviews 27 articles that assess climate risk in supply chains.
Findings
The literature on supply chain risks of climate change using quantitative techniques is limited. Our review confirms that no research adopts sustainability assessment methods to assess climate risk at a business-level.
Originality/value
Alongside the need to quantify physical risks to businesses is the growing awareness that climate change impacts traverse global supply chains. We review the state of the literature on methodological approaches and identify the opportunities for researchers to use sustainability assessment methods to assess climate risk in the supply chains of an individual business.
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Volodymyr Novykov, Christopher Bilson, Adrian Gepp, Geoff Harris and Bruce James Vanstone
Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a…
Abstract
Purpose
Machine learning (ML), and deep learning in particular, is gaining traction across a myriad of real-life applications. Portfolio management is no exception. This paper provides a systematic literature review of deep learning applications for portfolio management. The findings are likely to be valuable for industry practitioners and researchers alike, experimenting with novel portfolio management approaches and furthering investment management practice.
Design/methodology/approach
This review follows the guidance and methodology of Linnenluecke et al. (2020), Massaro et al. (2016) and Fisch and Block (2018) to first identify relevant literature based on an appropriately developed search phrase, filter the resultant set of publications and present descriptive and analytical findings of the research itself and its metadata.
Findings
The authors find a strong dominance of reinforcement learning algorithms applied to the field, given their through-time portfolio management capabilities. Other well-known deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN) and its derivatives, have shown to be well-suited for time-series forecasting. Most recently, the number of papers published in the field has been increasing, potentially driven by computational advances, hardware accessibility and data availability. The review shows several promising applications and identifies future research opportunities, including better balance on the risk-reward spectrum, novel ways to reduce data dimensionality and pre-process the inputs, stronger focus on direct weights generation, novel deep learning architectures and consistent data choices.
Originality/value
Several systematic reviews have been conducted with a broader focus of ML applications in finance. However, to the best of the authors’ knowledge, this is the first review to focus on deep learning architectures and their applications in the investment portfolio management problem. The review also presents a novel universal taxonomy of models used.
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This study provides a configurational examination of how policy designs influence the innovation performance of the emergency industry in China.
Abstract
Purpose
This study provides a configurational examination of how policy designs influence the innovation performance of the emergency industry in China.
Design/methodology/approach
This study employs the Data Envelopment Analysis Malmquist index (DEA-Malmquist) to quantify the innovation performance of the emergency industry and then codes the innovation policies to calculate the syntactic components based on institutional grammar tools (IGTs). The configurations of syntactic components were determined by applying the fuzzy-set qualitative comparative analysis (fsQCA).
Findings
The results indicate that rules- and norms-oriented policy designs would improve the innovation performance of China's emergency industry. In the developed provinces, the “Deontic” and “aIm” combinations in the policy are useful for improving performance. In the developing provinces, the ambiguity of the “aIm” and “Context” conditions in the policy is leading to low performance. Additionally, a lack of strategy-oriented policy design would also result in poor performance.
Originality/value
Most previous studies used substitute variables to understand policy impacts. This study contributes to identifying the impacts of the syntactic components of policy designs on the innovation performance of the emergency industry. The findings can assist policymakers in developing more effective policies to stimulate innovation development in the emergency industry.
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