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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.

The input-output method apply to multiregional tables from Eora-26 to decompose the GHG emissions of the Brazilian and Chinese productive structure.

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.

The metrics did not capture emissions related to land use and deforestation, which are representative of Brazilian emissions.

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.

The Spain–Portugal Cross-Border Cooperation Program launched the “Espomar” project in 2017, with the aim of designing a sustainable and transboundary maritime transport system in the Gulf of Cádiz, which includes the Spanish provinces of Cádiz (its Atlantic coast) and Huelva, together with the Portuguese region of the Algarve. This chapter limits the scope to the Algarve-Huelva connection and the resident population demand. The findings suggest a maritime connection defined and marketed as part of leisure activities. The demand for potential tourism packages created around boat trips appears to be promising. The sun beach, gastronomy wine and culture landscape vectors are the main attractors.

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

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.

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.

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.

The purpose of this paper is to settle the methodological debate on the decomposition of value added in gross exports, proposing a standard, exposing the drawbacks of the alternatives and quantifying the differences.

This paper systematizes the analytical framework and assesses and quantifies the various methodologies and its main differences.

The decomposition method of Borin and Mancini (2023), using a source-based approach and an exporting country perspective, should be considered as the standard for decomposing the value added in gross exports. This study finds that alternative approaches and perspectives are methodologically inferior, and that tailored perspectives do not provide an increase in accuracy that compensates their drawbacks.

This paper’s contribution is fourfold: it rejects the alleged equivalence between approaches and perspectives, defending the superiority of a particular method, approach and perspective; it gives quantitative examples of the differences between them; it proves that the drawbacks of tailored perspectives do not compensate their alleged accuracy (as they do not result in big quantitative differences with the standard perspective); and it argues that no valid standard decomposition can forego the calculation of value added exported, which requires the expression of exports in terms of final demand.

Digital labor platforms (DLPs) are transforming the nature of the work for an increasing number of workers, especially through extensively employing automated algorithms for performing managerial functions. In this novel working setting – characterized by algorithmic governance, and automatic matching, rewarding and punishing mechanisms – gig-workers play an essential role in providing on-demand services for final customers. Since gig-workers’ continued participation is crucial for sustainable service delivery in platform contexts, this study aims to identify and examine the antecedents of their working outcomes, including burnout and engagement.

We suggested a theoretical framework, grounded in the job demands-resources heuristic model to investigate how the interplay of job demands and resources, resulting from working in DLPs, explains gig-workers’ engagement and burnout. We further empirically tested the proposed model to understand how DLPs' working conditions, in particular their algorithmic management, impact gig-working outcomes.

Our findings indicate that job resources – algorithmic compensation, work autonomy and information sharing– have significant positive effects on gig-workers’ engagement. Furthermore, our results demonstrate that job insecurity, unsupportive algorithmic interaction (UAI) and algorithmic injustice significantly contribute to gig-workers’ burnout. Notably, we found that job resources substantially, but differently, moderate the relationship between job demands and gig-workers’ burnout.

This study contributes a theoretically accurate and empirically grounded understanding of two clusters of conditions – job demands and resources– as a result of algorithmic management practice in DLPs. We developed nuanced insights into how such conditions are evaluated by gig-workers and shape their engagement or burnout in DLP emerging work settings. We further uncovered that in gig-working context, resources do not similarly buffer against the negative effects of job demands.

This research study aims to delve into the enduring relationship between housing property prices and economic policy uncertainty across eight major Indian cities.

Using the panel non-linear autoregressive distributed lag model, this study meticulously investigates the asymmetric impact of economic policy uncertainty on apartment and house (unit) prices in India during the period from 2000 to 2022.

The findings of this study indicate that economic policy uncertainty exerts a negative influence on property prices, but noteworthy asymmetry is observed, with positive changes in effect having a more pronounced impact than negative changes. This asymmetrical effect is particularly prominent in the case of unit prices.

This research reveals that long-run price trends are also influenced by factors such as interest rates, building costs and housing loans. Through a comprehensive analysis of these factors and their interplay with property prices, this research paper contributes valuable insights to the understanding of the real estate market dynamics in Indian cities.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

This study aims to explain the effect of research and development (R&D) incentives on economic growth, focusing on the case of Türkiye. A one-sector endogenous growth model has been constructed. The model includes three actors: firm, consumer and government. The consumer derives utility from consumption, supplies human capital and engages in saving. The representative firm invests in R&D to maximize the current value of profit flows by choosing how much input it will use and how much R&D it will undertake. The public sector provides incentives for labor and capital used in R&D production. R&D has been defined as a function that endogenously increases total factor productivity (TFP).

In line with the stated purpose, this study presents a dynamic general equilibrium model. Then, this study calibrates the model parameters with Türkiye's data.

The results imply that incentives for R&D personnel instead of physical capital have a stronger impact on economic growth.

The findings of this study point to an important conclusion on how to distribute R&D incentives across the two main factors in R&D production, labor and capital. Incentives given to R&D personnel are more effective in Türkiye.

This study shows that the R&D incentives provided by the public sector can be important in emerging countries where many firms have just started their R&D activities. In this study, the authors worked on Türkiye as an emerging country. This study discusses policies on how the R&D incentives will be more effective on economic growth in Türkiye. This study considers that these policies may apply to all emerging countries, due to similar R&D activities in countries that cannot export technology and mostly import technology.

本研究擬以土耳其的實例為焦點, 探討研究與開發 (研發) 的激勵如何影響經濟的增長;具體地說, 研究旨在探討透過不同生產要素所提供的研發激勵所產生的影響存在著什麼差異。

為達研究目的, 研究人員構建了一部門內生增長模型。模型內有三個參與者: 公司、消費者和政府。消費者從消費中得到他們所需要的, 提供人力資本, 並參與儲蓄的活動。為了要把利潤的現值儘量提高, 代表公司透過調控投入的數量和研發的承擔, 投資在研發上。公共部門會為研發生產上使用的勞工和資本提供激勵。研究與開發被解釋為一個以內生方式增加全要素生產率的功能。構建的模型是因應土耳其的經濟狀況而調整出來的, 當中也進行了仿真模擬。

研究結果暗示, 為研發人員提供的激勵, 而不是物質資本, 更能推動經濟增長。

研究結果, 就如何於研發生產的兩個主要因素之間, 即勞工與資本之間, 分配研發激勵的問題上, 提供了重要的結論;就土耳其而言, 分配給研發人員的激勵是更為有效的。

我們展示了在新興國家裏, 公共部門提供的研發激勵是重要的, 而在這些國家裏, 剛開始進行研發活動的公司為數不少。在本研究裏, 我們把土耳其當作新興國家看待。我們討論了若要在土耳其使研發激勵更有效地幫助推動經濟增長, 什麼政策是最合適的呢? 因為那些不能把技術出口到其它地方, 而主要靠引進技術的國家均進行相似的研發活動, 所以我們認為討論得來的政策是可應用於所有新興國家的。

The integration of the digital economy and the real economy has been a key focus in promoting digital economic development. It denotes a comprehensive digital transformation of national economic activities regarding technological infrastructure and production modes, which is crucial for establishing a modern economic system, advancing industrial infrastructure and modernizing industrial chains.

Firstly, the study delves into the internal logic behind the emergence of the new development dynamic resulting from digital technology's evolution. Secondly, it explores the mechanism of mutual promotion and support between the new development dynamic and the digital economy based on China's shift in focus from international engagement to the domestic economy during different stages of industrialization. Subsequently, it analyzes the characteristics and critical factors of digital economy development and examines the macro-, meso- and micro-level constraints on these factors. Finally, the paper explores approaches to promoting digital economy development while constructing the new development dynamic and provides relevant policy suggestions.

The construction of the new development dynamic and the development of the digital economy are inextricably linked, and only by mutually reinforcing each other can they provide an inexhaustible impetus for China's high-quality economic development.

The new development dynamic and the digital economy development form an indivisible whole. The new development dynamic creates the necessary conditions for digital economy development and promotes the formation of digital production modes. In turn, the development of the digital economy should strive to improve the mainstay position of the domestic economy, enhance the synergy between the domestic economy and international engagement, upgrade value chains while improving the supply and the industrial chains in China and ensure a parallel increase in labor income alongside improved productivity.

This paper aims to establish the determinants of production in the Spanish Designation of Origin (DO) area for Cava wine and forecasts sales to establish vineyard area variations that maintain market equilibrium.

By applying a vector autoregressive (VAR) model, the authors forecast demand and the consequent requirements for base wine production.

The results show that Cava sales determine the base wine supply. After forecasting demand and the consequent requirements for base wine, the authors’ results show that, to avoid oversupply, the vineyard area for Cava wine should not be increased.

The paper develops a simple and effective method for DOs affected by the current European wine plantation regulations to forecast from a supply and demand perspective and their surface needs in response to market changes.

This study contributes to the literature because, to the best of the authors’ knowledge, no other study has investigated the determinants of Cava supply and demand or defines a model to assess the effects of changes in growing areas. The model is applicable to other European protected designations of origin wines and would help policymakers to accurately establish vine planting authorizations.