Synergy between sectoral output, energy and CO2 emission: a panel cointegration analysis of South Asia

Purpose The purpose of this study is to investigate the synergy between sectoral output, energy use and CO2 emission with other factors for a panel of South Asian economies including Afghanistan, Bangladesh, Bhutan, India, Pakistan, Maldives, Nepal and Sri Lanka. Design/methodology/approach The analysis is done using annual panel data from 1980–2019 using dynamic ordinary least squares (DOLS), fully modified OLS (FMOLS) and Toda-Yamamoto techniques. Findings Empirical findings reveal the existence of a statistically significant long-run cointegrating relationship between energy use, sectoral output such as agricultural, industry and service gross domestic product (GDP), globalization, urbanization and CO2 emission. DOLS and FMOLS result posits that in the case of the South Asian region agriculture GDP does not contribute to increasing CO2 emission while service and industrial GDP is responsible for increasing CO2 emission along with urban population, energy use and to some extent globalization. More remarkably, the contribution of the service GDP is greater than the other two sectoral outputs in increasing CO2 emission with a feedback hypothesis. Practical implications As CO2 emission is a global phenomenon with a cross-boundary effect, these empirical findings might contribute to formulating implementable energy and environmental policies to sustain growth, as well as to protect the environment in the regional context. Originality/value The study contributes to the literature by providing an empirical investigation of South Asia incorporating the contribution of sectoral output to understand the potential contribution of each sector on energy and emission. This is the first study on the South Asian context from the perspective of sectoral output, energy and emission.

Climate Change Impacts on Agriculture in Latin America and the Caribbean: An Application of the Integrated Economic-Environmental Modeling (IEEM) Platform

In this paper, we assess the economy-wide impact of Climate Change (CC) on agriculture and food security in 20 Latin American and the Caribbean (LAC) countries. Specifically, we focus on the following three channels through which CC may affect agricultural and non-agricultural production: (i) agricultural yields; (ii) labor productivity in agriculture, and; (iii) economy-wide labor productivity. We implement the analysis using the Integrated Economic-Environmental Model (IEEM) and databases for 20 LAC available through the OPEN IEEM Platform. Our analysis identifies those countries most affected according to key indicators including Gross Domestic Product (GDP), international commerce, sectoral output, poverty, and emissions. Most countries experience negative impacts on GDP, with the exception of the major soybean producing countries, namely, Brazil, Argentina and Uruguay. We find that CC-induced crop productivity and labor productivity changes affect countries differently. The combined impact, however, indicates that Belize, Nicaragua, Guatemala and Paraguay would fare the worst. Early identification of these hardest hit countries can enable policy makers pre-empting these effects and beginning the design of adaptation strategies early on. In terms of greenhouse gas emissions, only Argentina, Chile and Uruguay would experience small increases in emissions.

Structural Transformation Path Across Countries: Is It Systematic Pattern

The structural transformation across the economic sectors is one of the prominent features that go together with economic development. The paper scrutinizes whether developing and low-income countries follow the similar path and pattern of structural transformation by which the developed countries are following or followed this threshold and are now experiencing a shift from the industrial sector to the service sector. The structural transformation paths of developed countries are almost identical, but the pattern of sectoral output shares varies from that of developing countries. The research reveals a fascinating finding i.e., low-income countries outperform middle-income countries and some major countries in terms of the pace of structural transformation from agriculture to service sector.

Renewable Energy Consumption and Sectoral Based Output

The study explores the effect of renewable energy consumption on sectoral output in the presence of government effectiveness. A regressions method was used to analyze data from 1989 to 2019. The result revealed evidence of the positive and vital impact of renewable energy consumption on the sectoral output of the manufacturing and construction sectors. Although the elasticity of government effectiveness is neutral, trade openness has revealed evidence of positive and significant impact on sectoral outputs. However, population growth does not have a favourable impact on sectoral outputs. Furthermore, renewable energy consumption is not essential in determining the agricultural sector, transportation sector and other sectors. To quickly diversify the economy, policymakers should further increase awareness and provide more incentives for renewable energy in these sectors

Energy Consumption and Bi-Sectoral Output in Pakistan: A Disaggregated Analysis

The objective of study is to check the vigorous impact of energy consumption on industrial and agricultural output with disaggregated analysis by having openness in both sectors and tube wells lone in agriculture sector as controlled variables. It is essential to analyze a connection between energy consumption and bi-sectoral output in Pakistan. Industrial and agricultural outputs have been taken as dependent variable, as they are mainly dependent on energy consumption. The data from 1999-2019 is employed for the analysis. The econometric technique autoregressive distributed lag (ARDL) results are showing a strong bond between energy consumption and industrial output in disaggregated relationship. Electricity shows a negative relationship with industrial output because of developing countries power supply failure dilemma. Similarly, agriculture sector shows significance with energy consumption in disaggregated analysis. Openness of agriculture and gas consumption in agriculture shows a negative but statistically significant relationship. Capital and labor in both sectors are highly influencing regressors as par neo classical output theory, in our disaggregated energy consumption analysis. Error correction regression shows a strong short run and long run relationship of energy consumption with industrial and agricultural output. The stability diagnostic recursive estimates show the perfectly interlinked variables in both models. The present research is equally important for the academic and policy makers as it reveals a strong bond between energy consumption and bi-sector output in Pakistan. Potential measures on energy supply can increase industrial and agricultural output.

A novel approach for measurement and decomposition of the economywide costs of shutting down tourism and related service sectors against COVID-19

The trade-off between desirable public health outcomes and undesirable economic outcomes of anti-pandemic measures forces policy makers everywhere to seek the right combination of measures to balance the public health concerns against employment and income considerations. This article describes a novel input–output approach to assessing economywide costs of shutting down tourism and related sectors to curb the spread of COVID-19. Our framework allows for a decomposition of the total effects of shutdowns into sectoral output losses resulting from (i) suspension of the delivery of inputs to other sectors, (ii) termination of the demand for inputs produced by these sectors, and (iii) the interruption of payments to the owners of factors of production employed in the sectors ordered to shut down. We illustrate the use of this methodological framework to measure and decompose the effects of recent shutdown orders issued in Turkey, a country of major tourism activity.

Should the United States Rejoin the Trans-Pacific Trade Deal?

Before the Trans-Pacific Partnership (TPP) entered into force, the United States withdrew from the trade accord. Eleven other TPP signatories decided to revive the agreement, which led to the implementation of the Comprehensive and Progressive Agreement for TPP (CPTPP). The objectives of this paper are to estimate economic welfare effects under alternative scenarios of TPP/CPTPP, to evaluate the extent of losses to the US from its withdrawal from TPP and expected gains from rejoining the Trans-Pacific trade accord, and to examine whether the US economy would have to undergo extensive sectoral adjustments from its participation. We employ a dynamic computable general equilibrium (CGE) model to examine these issues. The results suggest that the US loses an opportunity to gain 0.4 percent in its economic welfare by withdrawing from TPP, but it would be able to recover most of its projected welfare gains by reengaging with CPTPP. Since sectoral output adjustments in the US are small, its adjustment costs from participation in CPTPP would be limited. In addition, there exist political incentives for the US to become a member of this trade accord.

A new approach to modelling the input–output structure of regional economies using non-survey methods

This paper proposes a new approach to the regionalization of national input–output tables where suitable regional data are scarce and analysts are considering using location quotients (LQs). We focus on the FLQ formula, which frequently yields the best results of the pure LQ-based methods, and develop an enhanced way of implementing this approach. We use a modified cross-entropy (MCE) method, along with a regression model, to estimate values of the unknown parameter δ in the FLQ formula, specific to both region and country. An analysis of survey-based data for 16 South Korean regions reveals that the proposed FLQ+ approach yields more accurate estimates of both input coefficients and sectoral output multipliers than those from simpler LQ-based methods or the MCE approach alone. Sectoral outputs (or employment) are the only regional data required. The MCE method also clearly outperforms GRAS.