Assessment of Best Performing Top 10 Economies Based on OECD Green Growth Indicators: Using AHP, TOPSIS and Regression

Abstract

Rapid economic growth is often accompanied by environmental depletion. Recent years have witnessed green growth as a renowned efficient approach to tracking the progress towards sustainable development. Green growth is imperative with the current energy depletion rate and environmental crisis. Green indicators and statistics can measure environmentally sustainable development; thus, they evaluate green growth and support its integration into policy. This study uses MCDM (Multi-criteria decision-making) techniques and OECD indicators for the world’s top 10 economies to evaluate their green growth. This research aims to rank the top 10 economies according to green growth. The top-ranked best-performing country will be further analyzed to assess its renewable energy production.

Introduction

I.  INTRODUCTION

The accelerating pace of climate change has added another dimension of complexity to the relationship between economic growth and environmental degradation, leading to calls for policies to achieve a balance between the two. In addition to altering normal weather conditions, climate change will likely alter the frequency and intensity of extreme weather events, threatening human safety, health, and survival worldwide. Because of the common set of threats it poses on a global scale, a response to the challenge of climate change must emerge out of a common multilateral participative agenda, a worldwide resolve geared toward combating the effects of climate change.

Fossil fuel burning by the world’s richest nations and their citizens’ consumption habits cause half the global deaths from fine-particle pollution [1] G-20 countries make up nearly two-thirds of the global population, 80 percent of the world’s economic output and three-quarters of international trade. They also have large environmental footprints when it comes to consumer products, with most developing countries manufacturing these goods and suffering the health impacts that arise from these operations [2]

Several countries and international organizations, such as the Organisation for Economic Co-operation and Development (OECD), World Bank, and United Nations, have developed strategies for green growth; others, such as the Global Green Growth Institute (GGGI), are specifically dedicated to the issue. To evaluate the performance of different countries, this paper takes the world’s 10 top fastest-growing economies to find which fast-growing economy in the world is performing best in sustainable green growth

A. Identification of Suitable Green Growth Indicators

The assessment of the level of Green Growth is based on OECD Green Growth Indicators. These indicators are in the OECD Green Growth database (OECD 2017). It contains selected indicators for monitoring progress towards Green Growth. The database synthesizes data and indicators across a wide range of domains.

The indicators have been selected according to well-specified criteria and embedded in a conceptual framework, which is structured around five areas to capture the main features of the Green Growth

1. environmental and resource productivity and innovation;
2. natural assets (including biodiversity) and their cost-effective management;
3. the environmental quality of life (including access to basic services such as clean water);
4. related green growth policies, economic opportunities and social context of green growth; and
5. monitoring sustainability of overall economic developments, for example, through comprehensive wealth accounting

Table 1 Indicators

B. Literature Review

Green growth means fostering economic growth and development while ensuring that natural assets continue to provide the resources and environmental services on which our well-being relies. (OECD 2017) The focus of green growth strategies is ensuring that natural assets can deliver their full economic potential on a sustainable basis. That potential includes providing critical life support services – clean air and water, and the resilient biodiversity needed to support food production and human health. Natural assets are not infinitely substitutable, and green growth policies consider that. Researchers like Meng Li have researched inclusive green growth in Asia-Pacific [3]. Saira Naseer has assessed green economic efficiency in china using AHP, and the research indicates that the AHP model reduces uncertainty and shortcoming in the evaluation process [4]. Research comparing the green economy in Iran with OECD countries strongly suggests using some other MCDM techniques such as AHP, ELECTRE, and Fuzzy MCDM [5]. Piwowarski Mateusz used TOPSIS and VIKOR methods to study sustainable development in EU countries [6]. To avoid developing unfavorably, “green growth” suggests pursuing such an economic development through which we can avoid degradation of the environment, loss of biodiversity, and unmanageable usage of natural resources (OECD, 2017). Green growth strongly emphasizes the fostering conditions needed to innovate, invest, and compete to stimulate economic growth without derailing resilient ecosystems [9].

Green growth is synonymous with renewable energy production. Despite the world's top economies being the main perpetrators of exploiting resources and the environment, their efforts towards green growth should be amplified. Previous researches lack the conceptual approach using MCDM techniques for assessing green growth while including renewable energy production. This research aims to evaluate the world’s top 10 economies using MCDM techniques, including AHP, TOPSIS, Simple moving average & Regression, while analyzing the best-performing country’s renewable energy production. 

II. METHODS AND MODELS

A. Analytic Hierarchy Process (AHP)

In the decision-making theory, the analytic hierarchy process (AHP) is a structured technique for organizing and analyzing complex decisions based on mathematics and psychology. Thomas L. Saaty developed it in the 1970s [7].

AHP follows the following steps,

1. The hierarchical process of the problem is formed.
2. Pair-wise comparisons are conducted for the criteria, sub-criteria, and alternatives.
3. Weights of pair-wise comparisons are calculated.
4. After calculating weights, decision matrixes are formed.
5. Final weights of alternatives obtain by multiplying decision matrixes from alternatives toward criteria.
6. Weights obtained in the last step are raw and must be normalized to be easily comparable.
7. Calculate the consistency ratio (CR). Correct the consistency of comparison. If CR is less than 0, the consistency is good. Otherwise, it is inconsistent. In this case, you need to adjust the comparison component to get good consistency.

Conclusion

In this research, we have considered the world’s top 10 best-performing economies. Global warming and climate change are serious issues. It is noted that the impact made by top economies on climate and environment is higher than the developing nations. Hence the leaders of world economies should focus on green growth. This research is divided into two objectives. The initial part of the research focuses on ranking the top 10 best performing economies based on OECD indicators using AHP and TOPSIS. This study aims to introduce a novel approach based on AHP and TOPSIS to rank and evaluate the indicators of green growth into sustainable growth. A combination of AHP and TOPSIS models has been used to evaluate and rank the green growth indicators based on experts\' opinions and literature review. In this regard, six indicators and seven sub-indicators are collected from the existing green growth literature. The data for evaluating green growth is collected from OECD Statistics. The results from AHP indicate that renewable energy production and production-based CO2 emission carry the highest value in green growth. The obtained weights from AHP were used in TOPSIS analysis, indicating that Germany is the best performing nation in green growth while India and China rank the lowest among the top 10. These results are consistent with the OECD annual report as most countries have set up targets to decrease emissions and increase the use of renewable sources by 2030; Germany is the only country in the OECD vision 2030 which is performing well along its set targets. The second part of the thesis focuses on the energy production of Germany and correlates renewable sources and total energy production. And forecast renewable production and gross production. Simple moving averages and regression has been used for this task. Using a Simple moving average, the renewable energy production is forecasted and used in the formulated equation driven by the regression. This result shows that solar energy production negatively impacts Germany\'s total energy production. This research suggests that for Germany to improve further towards green growth, it should focus on hydropower and biofuels. Despite the study\'s strengths, some limitations can be opportunities for future work. First, this study evaluated the green growth indicators in only the top 10 economies, but the factor of developing nations and developed nations is not considered in this study; therefore, future works would extend this method in evaluating the factors and differences between the developing countries and developed countries. Secondly, performance measurement on green growth for both sustainable performance and operational performance can be considered the critical drivers for ascertaining the success and failure of green growth. Therefore, future studies would measure green growth indicators based on different performance measurements [10], such as the Global Reporting Initiative (GRI) sustainable performance measurement. This study used the quantitative approach for evaluating green growth indicators; therefore, the integration of qualitative research and quantitative research will strengthen the study results to clarify the impacts of green growth policy on the practices.

References

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Copyright

Copyright © 2022 Muhammad Mughees, Muhammad Ayaz. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.