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MindMap Gallery Applying a three-stage SBM-DEA model to evaluate energy efficiency and impacts in RCEP countries

Applying a three-stage SBM-DEA model to evaluate energy efficiency and impacts in RCEP countries

This is a mind map on applying the three-stage SBM-DEA model to evaluate the energy efficiency and impact of RCEP countries, including research methods, results and discussions, comparative analysis, etc.

Edited at 2024-01-29 01:41:43

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Applying a three-stage SBM-DEA model to evaluate energy efficiency and impacts in RCEP countries

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Outline

Applying the three-stage SBM-DEA model to evaluate energy efficiency and influencing factors in RCEP countries

introduction

Research Background

As a key strategic resource, energy is one of the basic driving forces for economic growth and a key factor in a country's future development. In recent years, the study of energy efficiency and its influencing factors has become a hot topic, especially in the context of pursuing the concept of green development, and its in-depth analysis is of great significance.

Research questions and objectives

RCEP countries occupy an important position in terms of energy consumption and carbon dioxide emissions, but energy efficiency is still relatively low relative to the size of the economy. The article puts forward research questions and objectives, including a comprehensive analysis of the current status, trends, and influencing factors of RCEP energy efficiency, as well as a discussion of the differences in energy efficiency in different countries and how to optimize and improve it.

Significance

Research on regional energy efficiency is key to solving global energy problems. Emphasizing that in-depth analysis of RCEP’s energy consumption and putting forward relevant suggestions for improving energy efficiency are of great significance to promoting green development in the region

methodology

The three-stage SBM-DEA model analyzed the energy efficiency and influencing factors of 13 RCEP countries from 2000 to 2015.

literature review

Sun et al. used stochastic frontier analysis (SFA) to analyze 25 years of energy data from 71 economies and concluded that green innovation has a positive impact on energy efficiency.

Background 1

Background 2

Background 3

Honma and Hu applied SFA to explore energy efficiency and influencing factors in 41 administrative regions in Japan.

Purpose 1

Purpose 2

Purpose 3

1. The diversity of production functions. Choosing a production function will lead to systematic errors. 2. The SFA method cannot solve the situation of multiple inputs and multiple outputs.

From a business perspective, Moon and Mon used the DEA method to evaluate South Korea's energy efficiency

Method summary 1

Method summary 2

Method summary 3

Liu et al. applied the traditional DEA-BCC model to reexamine provincial energy efficiency in China.

Technology 1

Technology 2

Technology 3

Tone proposed a slack-based metric DEA (SBM-DEA) to incorporate undesired outputs into efficiency evaluation.

Result 1

Result 2

Result 3

Wu et al. used a three-stage DEA method to analyze the electrical energy efficiency of China's steel industry and concluded that electrical energy efficiency gradually decreases from east to west.

Cui and Li applied three-stage virtual frontier DEA to evaluate transportation energy efficiency.

Combining SFA and DEA, a multi-stage DEA model is proposed

Research methods

DEA model

SBM-DEA model

Poorly output SBM-DEA model

Three-stage DEA model

The DEA model does not consider the impact of environmental factors and random errors, so the calculation results are biased

The first stage

Use the traditional DEA model to obtain the efficiency value and input slack of each DMU based on the original input and output data.

second stage

The SFA regression equation is constructed using the input slack and environmental variables obtained in the first stage. The purpose of this step is to eliminate the influence of external environment and random errors

In order to adjust the input values, the random error term needs to be separated from the composite error term of the SFA model. Management inefficiencies are first separated using the following formula:

Further decomposition of the random error term

The adjustment formula for input variables is

The third phase

Combining the adjusted input and original output, the DEA model is applied again to obtain relatively accurate efficiency values after eliminating external environment and random errors.

Construction of three-stage SBM-DEA model

Traditional DEA does not add slack variables to the objective function, which is likely to lead to biases in the measurement results caused by radial and angular

The SBM-DEA model avoids errors caused by subjective selection of radial and angle, and solves the problem of slack input and output variables.

The biggest difference between the three-stage DEA model and the traditional DEA model is that the three-stage DEA model takes into account the impact of environmental factors and random noise on the results.

Build steps

Based on the original input-output data, the SBMDEA model was used to measure the energy efficiency of 13 RCEP countries.

Use SFA to adjust input

Combining the adjusted input and raw output data, the SBM-DEA model is again applied to measure energy efficiency.

Indicator selection

input variables

share capital

labor force

Energy consumption

Per capita energy consumption per country multiplied by total population

output variable

gross domestic product

ideal output

Carbon dioxide emissions

undesirable output

environment variables

①Industrial structure

The adjustment of industrial structure will affect the proportion of the three major industries and have a greater impact on energy consumption. This article expresses the industrial structure in terms of industrial added value (% of GDP).

②Urbanization level

The process of urbanization has a specific impact on energy supply and demand, thus affecting energy efficiency. This article chooses the ratio of urban population to the total national population to represent the urbanization process.

③Energy consumption structure

Different energy consumption has a significant impact on carbon dioxide emissions. The energy consumption structure is represented by the proportion of fossil energy consumption in total energy consumption.

④Commodity trade

The degree of openness to the outside world will affect the production structure of products, thereby changing the level of energy consumption. This article chooses the proportion of commodity trade in national GDP to represent the commodity market.

⑤Government efficiency

Corresponding policies proposed by the government may be beneficial to energy conservation. However, excessive government intervention may affect the efficient allocation of resources, thereby affecting energy efficiency

⑥ GDP per capita

This indicator is used to measure a country's level of development, which will affect energy efficiency

⑦Tourism revenue

RCEP countries are rich in tourism resources. As a tertiary industry, tourism and economic growth are conducive to improving energy efficiency.

Results and discussion

The first stage

At this stage, the influence of external environment and random errors is not considered. The SBM-DEA model was used to calculate the energy efficiency of 13 RCEP countries from 2000 to 2015.

The energy efficiency of developed countries (Australia, New Zealand, Japan, Singapore) is generally better than that of developing countries, with the exception of South Korea. Developed countries have advantages such as high technological level and high human development index, while developing countries like Brunei are an exception. Their high income level affects their relatively high energy efficiency.

second stage

Analysis of SFA regression results

Environmental factors will affect the energy efficiency of RCEP countries. Therefore, each country's inputs are adjusted through equations (4)-(7) so that they face the same external environment and random errors.

The third phase

Enter adjusted energy efficiency

comparative analysis

Comparative analysis of energy efficiency in the first and third stages

Decline in average energy efficiency: The average energy efficiency of RCEP dropped from 0.638 in the first stage to 0.384 in the second stage, a decrease of 39.8%. This indicates that external environmental factors have an impact on the energy efficiency of RCEP, leading to an overestimation of energy efficiency in the first stage.

Country differences

China, Japan and South Korea: 1. China achieved significant energy efficiency improvement in the second stage, rising from 0.290 to 0.953, an increase of 228.6%. 2. China’s previous low efficiency was related to the relatively poor external environment, while the green economy and clean development policies in recent years have led to improvements in energy efficiency. 3. As developed countries, Japan and South Korea have relatively little impact on the external environment, and their energy efficiency remains at a high level at all stages.

Changes in other countries: Energy efficiency declines in 10 countries The economic and social development in various aspects of developed countries such as Australia, New Zealand and Singapore have helped create a good external environment and improved energy efficiency. Among developing countries, Brunei and Cambodia experienced the largest declines, at 98.0% and 96.9% respectively. Brunei's high per capita GDP and social welfare have formed a good external environment and improved energy efficiency. Indonesia: Indonesia has the smallest decrease in energy efficiency, indicating less impact from the external environment, possibly related to lower levels of management.

Comparative analysis of DEA-BCC model and SBM-DEA model results with bad output

1. The energy efficiency calculation results of the DEA-BCC model are higher than those of the SBM-DEA model. 2. The DEA-BCC model may have overestimated the energy efficiency of RCEP. 3. The calculated coefficients of variation show that they are 0.218 (DEA-BCC) and 0.490 (SBM-DEA model) respectively, so the measurement results of the SBM-DEA model have a high degree of dispersion and have a strong ability to judge energy efficiency.

The three-stage SBM-DEA model was found to have significant advantages in energy efficiency measurement.

Summarize

methodology

A three-stage SBM-DEA model was used to estimate the energy efficiency of 13 RCEP countries from 2000 to 2015. Capital stock, labor and energy consumption are used as input variables, GDP as desirable output, and carbon dioxide emissions as undesirable output.

SFA then eliminates the influence of external environment and random errors on energy efficiency.

in conclusion

Based on the results of the first phase. The energy efficiency of the 13 RCEP countries fluctuated between 2000 and 2015, first rising and then falling. Australia, Brunei and New Zealand are highly energy efficient, while China and Vietnam are the least energy efficient. Developed countries are more energy efficient than developing countries.

Based on the results of the second phase. External environmental factors have a significant impact on energy efficiency.

After eliminating the interference and random errors of the external environment, the average energy efficiency of RCEP in the third stage is lower than that in the first stage, indicating that external environmental factors will lead to overestimation of the energy efficiency of RCEP. However, changes in efficiency vary from country to country.

Contributions and prospects

contribute

The SBM-DEA model takes into account the undesirable output of carbon dioxide, and the three-stage DEA model eliminates the influence of environmental factors and random errors. Combining these two models can measure energy efficiency more accurately, which has certain theoretical significance for research in this field.

Previous studies have mainly focused on variables related to economic development and less on indicators representing national political conditions. This paper introduces political indicators of government efficiency into general economic indicators to examine the impact of national political conditions on energy efficiency. A more comprehensive analysis of energy efficiency is provided by taking into account political factors.

Introducing tourism revenue as an influencing factor, special attention is paid to the rich tourism resources of the top ten ASEAN countries. Tourism, as a tertiary industry, may improve energy efficiency by promoting economic growth. The research fills some gaps in the field of energy efficiency in RCEP and provides a new perspective for energy cooperation.

Future work outlook

This article only considers carbon dioxide emissions when selecting undesirable outputs. Future research can consider undesirable outputs such as SO2 and NOX.

Focus on important factors affecting energy efficiency

More new methods can be used to measure energy efficiency, such as the virtual frontier DEA model