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This paper examines the dynamics of structural transformation in Morocco since 1970 by analyzing input-output tables expressed in terms of employment and output levels across 24 sectors.

This study employs a twofold methodological approach. Firstly, it examines the evolution of sectoral employment shares over time using World Bank data. Secondly, it utilizes Input-Output analysis to examine structural shifts in Morocco's economy, focusing on sector-specific output and employment data. The primary data source is the Eora Global Supply Chain Database, covering the years 1970, 1980, 1990, 2000, and 2015. Additionally, to transition from production-based to employment-based input-output tables, the study leverages employment and output data from the Penn World Tables to calculate the diagonal labor coefficient matrix.

First, our analysis reveals that Morocco's economic transformation has been slower compared to high-income countries. Structural changes, as evidenced by the evolution of employment shares by sector, show a gradual decline in agricultural employment share over the period 1991-2019, accompanied by a shift towards the services sector. This shift, driven by favorable conditions in the services sector and increased capital use in agriculture, has resulted in premature deindustrialization. The industrial sector's employment share has remained stable due to its capital-intensive nature. Second, Input-Output analysis reveals a pronounced premature tertiarization of the Moroccan economy. Between 1990 and 2000, the tertiary sector saw a dramatic rise in both backward (167%) and forward (68%) linkages, while the primary sector's backward linkages fell by 33% during the same period. Although the primary sector’s linkages increased by 10% from 2000 to 2015, the secondary sector experienced a consistent decline in backward linkages, dropping 12% from 1990 to 2000 and an additional 10% from 2000 to 2015. Employment linkage analysis further underscores this shift, with a 12% increase in the tertiary sector’s backward linkages from 1990 to 2000, contrasted by significant declines in the primary (51%) and secondary (7%) sectors. These trends highlight an unsustainable move towards services without concurrent industrial development, challenging balanced economic development.

As it is unanimous, the structural transformation of Morocco remains relatively slow and characterized by a shift of the labor factor from the primary sector to the tertiary sector, with a limited job creation by the secondary sector considered as the pillar of any structural transformation. This paper advances the field of research on structural transformation by elucidating the premature tertiarization of the Moroccan economy and the slowness pace at which the transformation of its economic fabric is occurring, thereby filling the empirical gap.

Tourism is an essential industry in Indonesia, and understanding its inter-sectoral and inter-regional connections is critical for policy development. This study examines the economic impact of regional tourism in Indonesia and the connections between different tourism-related regions and industries.

This study uses a non-survey method to estimate the inter-regional input-output table (IRIOT) in 2019, backward and forward linkage to identify the role of tourism in the economy, and the structural path analysis (SPA) to identify the inter-sectoral and inter-regional flow of tourism effect. The benchmark IRIOT 2016 published by Badan Pusat Statistik (BPS) serves as the primary data source.

The findings indicate that tourism has a relatively high impact on the overall national economy and plays an essential role in nine provinces. However, this study uses four provinces to represent Indonesian tourism: Jakarta, Jawa Timur, Bali, and Kepulauan Riau. The SPA result captures that Kepulauan Riau Province has the highest tourism multiplier effect and Jawa Timur has the highest coverage value. Moreover, the manufacturing sector receives the most benefit from the tourism effect, followed by trade, construction, agriculture, transportation, and electricity-gas. From a spatial perspective, tourism connections are not solely based on geographical proximity. Instead, they are established through an intricate supply chain network of manufactured goods. This emphasizes the significance of considering supply chain dynamics when investigating inter-regional relationships in the tourism sector.

This research contributes to the literature by estimating the IRIOT in 2019, disaggregating tourism activities from related economic sectors, constructing tourism-extended IRIOT, and identifying the critical path of tourism effect in numerous provinces with different economic structures. This novel approach offers valuable insights into the full spectrum of tourism’s economic impact, which has not been previously explored in this depth. This study is useful for policymaking, investment insight, and disaster mitigation.

The study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.

Utilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.

Analysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.

This research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

Understanding China's carbon dioxide (CO2) emission status is crucial for getting Carbon Neutrality status. The purpose of the paper is to calculate two possible scenarios for CO2 emission distribution and calculated input-output flows of CO2 emissions for every 31 China provinces for 2012, 2015 and 2017 years.

In this study using the input and output (IO) table's data for the selected years, the authors found the volume of CO2 emissions per one Yuan of revenue for the industry in 2012 and the coefficient of emission reduction compared to 2012.

Results show that in the industries with a huge volume of CO2 emissions, such as “Mining and washing of coal”, the authors cannot observe the reduction processes for years. Industries where emissions are being reduced are “Processing of petroleum, coking, nuclear fuel”, “Production and distribution of electric power and heat power”, “Agriculture, Forestry, Animal Husbandry and Fishery”. For the “construction” industry the situation with emissions did not change.

“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 CO2 emissions, but they do not take products characterized by significant emissions from other provinces. Shandong produces a lot of emissions and consumes many of products characterized by large emissions from other provinces. However, Shandong showed a reduction in CO2 emissions from 2012 to 2017.

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.

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.

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.

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.

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.

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.

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.

(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.

Waste generation poses a significant environmental challenge in the United Arab Emirates due to the rapid urbanization, population growth and industrialization witnessed in recent decades. As a result, there has been a substantial surge in waste production. To fulfil its sustainability and circular economy aspirations in various economic domains, the UAE must prioritize efficient waste management. The purpose of this study is to assess the environmental and energy efficiency of the UAE’s economic sectors particularly within its vital energy sectors, which encompass crude oil, natural gas and mining, manufacturing and electricity, by gauging their adherence to sustainability and circularity objectives.

The authors used the data envelopment analysis input–output model to identify sectors that exhibit strong performance as well as those that are falling behind.

Based on this study, the agriculture, the crude oil, natural gas and mining sectors and financial services and banking were found to be the most efficient. The results of this study concluded that the UAE is making progress toward achieving its sustainability and circularity objectives; however, the findings suggest that more effort is needed to fully realize these goals.

By identifying high-performing and underperforming sectors, decision-makers can prioritize efforts to enhance sustainability and circularity in area of greatest need in the economy.

This study investigates the causal relationship and mechanisms between the development of digital finance and household carbon emissions. Its objective is to explore how digital finance can influence the carbon footprint at the household level, aiming to contribute to the broader understanding of financial innovations' environmental impacts.

The research combines macro and micro data, employing input-output analysis to utilize data from the China Household Finance Survey (CHFS) for the years 2013, 2015, 2017, and 2019, national input-output tables, and Energy Statistical Yearbooks. This approach calculated CO2 emissions at the household level, including the growth rate of household carbon emissions and per capita emissions. It further integrates the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) for 2012–2018 and corresponding urban economic data, resulting in panel data for 7,191 households across 151 cities over four years. A fixed effects model was employed to examine the impact of digital finance development on household carbon emissions.

The findings reveal that digital finance significantly lowers household carbon emissions. Further investigation shows that digital transformation, consumption structure upgrades, and improved household financial literacy enhance the restraining effect of digital finance on carbon emissions. Heterogeneity analysis indicates that this mitigating effect is more pronounced in households during the nurturing phase, those using convenient payment methods, small-scale, and urban households. Sub-index tests suggest that the broadening coverage and deepening usage of digital finance primarily drive its impact on reducing household carbon emissions.

The paper recommends that China should continue to strengthen the layout of digital infrastructure, leverage the advantages of digital finance, promote digital financial education, and facilitate household-level carbon emission management to support the achievement of China's dual carbon goals.

The originality of this paper lies in its detailed examination of the carbon reduction effects of digital finance at the micro (household) level. Unlike previous studies on carbon emissions that focused on absolute emissions, this research investigates the marginal impact of digital finance on relative increases in emissions. This method provides a robust assessment of the net effects of digital finance and offers a novel perspective for examining household carbon reduction measures. The study underscores the importance of considering heterogeneity when formulating targeted policies for households with different characteristics.

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.

This study examines the carbon emissions of Australia’s agriculture, forestry and fishing sectors from a consumption perspective to develop effective policy frameworks. The objective is to identify key supply chains, industries and products contributing to these emissions and provide recommendations for sustainable development.

A multiregional input-output lifecycle assessment was conducted using the Australian Industrial Ecology Virtual Laboratory (IELab) platform to disaggregate sectors and enable benchmarking against other economic sectors.

In 2018, the “agriculture, forestry, and fishing” sector was responsible for 12.15% of Australia’s carbon footprint. Major contributors included the “electricity, gas, water, and waste” category (26.1%) and the sector’s activities (24.3%). The “transport, postal, and warehousing” sector also contributed 18.4%. Within the industry, the agriculture subsector had the highest impact (71.3%), followed by forestry and logging (15%) and fishing, hunting and trapping (7.6%). Aquaculture and supporting services contributed 6.1%.

The principal constraint encountered by the present study pertained to the availability of up-to-date data. The latest accessible data for quantifying the carbon footprint within Australia’s agriculture, forestry and fishing sector, utilizing the Input-Output analysis methodology through the Australian Industrial Ecology Virtual Laboratory (IELab) platform, about 2018.

The findings of this study provide policymakers with detailed insights into the carbon footprints of key sectors, highlighting the contributions from each subsector. This information can be directly used to develop effective emission-reduction policies, with a focus on reducing emissions in utility services, transport and warehousing.

The study, by raising public awareness of the significant role of industrial agricultural methods in Australia’s carbon footprint and emphasizing the importance of renewable energy and sustainable fuels for electricity generation and road transport, underscores the urgent need for action to mitigate climate change.

This study stands out by not only identifying the most impactful industries but also by providing specific strategies to reduce their emissions. It offers a comprehensive breakdown of specific agricultural activities and outlines mitigation strategies for utility services, agricultural operations and transport, thereby adding a unique perspective to the existing knowledge.