Trang 1 FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS ---***--- MIDTERM ASSIGNMENT Module: International Economics 1 Trang 2 TABLE OF CONTENTABSTRACT...INTRODUCTION...1C
OVERVIEW OF THE TOPIC
The concept of trade openness
Trade openness describes a country's economic orientation, which can be either outward or inward Outward-oriented economies actively engage in international trade, leveraging opportunities for growth, while inward-oriented economies tend to miss or are unable to capitalize on these trade opportunities Key trade policy decisions influencing this orientation include the implementation of trade barriers, import-export regulations, infrastructure development, technological advancements, economies of scale, and market competitiveness.
Trade openness can be calculated by following formula:
The below chart illustrates trade openness indices of ASEAN countries:
Figure 1.1 Trade openness of ASEAN countries from 2007 to 2021 (Source: World Development
It can be seen that Singapore had the highest trade openness indices through the years The maximum value reached nearly 450% in 2018 and the trough was still higher compared to
Between 2007 and 2014, Malaysia led in trade openness with an index of 300%, but was later surpassed by Vietnam in subsequent years Most countries maintained trade openness indices ranging from 50% to 150%, while Myanmar and Indonesia recorded the lowest figures, falling below 50% Overall, the trade openness trends varied significantly across different countries.
Literature review
Renewable energy comes from natural sources that replenish faster than they are used, making it a sustainable option for generating electricity, heating and cooling water, and transportation Common renewable energy sources include solar, wind, geothermal, hydropower, ocean energy, and bioenergy, all of which are readily available in our environment.
Renewable energy offers significant advantages that positively impact the economy, environment, national security, and human health By harnessing renewable resources, we can reduce greenhouse gas emissions, create jobs, enhance energy independence, and promote sustainable development Transitioning to renewable energy sources not only mitigates climate change but also improves air quality and public health, making it a vital solution for a sustainable future.
Enhanced reliability, security, and resilience of the nation’s power grid Job creation throughout renewable energy industries
Reduced carbon emissions and air pollution from energy production
Increased affordability, as many types of renewable energy are cost-competitive with traditional energy sources
Expanded clean energy access for non-grid-connected or remote, coastal, or island communities.
The percentage of renewable energy in WB is considered the percentage of consumption on renewable energy, in total of final energy consumption
ASEAN's economy has experienced significant growth over the years, resulting in a substantial rise in energy demand The International Energy Agency (IEA) reports a 60% increase in ASEAN's energy consumption over the past 15 years, with projections indicating a further 66% rise in energy demand by 2040.
In ASEAN, Thailand has a great advantage in solar energy According to statistics, record investments are pouring into this sector The achievement is thanks to the Thai
For the past two decades, the Philippine government has implemented policies to promote renewable energy development, including a tax support program The Department of Energy aims to triple renewable energy production to 15,300 MW by 2030, contributing approximately 30% of the total new energy capacity Meanwhile, Indonesia possesses substantial geothermal energy potential, holding 40% of the world's geothermal resources, with around 276 geothermal production sites across the country.
Malaysia has emerged as the world's third largest producer of solar photovoltaic cells A study by MPRA indicates a short-run causal relationship where increased renewable energy consumption leads to higher imports and exports This highlights a growing trend in the production and consumption of renewable energies alongside international trade worldwide While the connection between renewable energy consumption and international trade is recognized, empirical research on this relationship remains limited (MPRA, 2013).
Energy intensity level refers to the amount of energy consumed to produce a single unit of output, where a lower energy requirement signifies reduced energy intensity Both renewable and non-renewable energy resources are essential for production, necessitating land use and labor In terms of economic prosperity, advancements in technology can enhance manufacturing efficiency and promote environmentally-friendly practices, ultimately leading to a decrease in energy intensity levels.
Economists have proposed various theories regarding the relationship between trade openness and energy intensity, forming the basis for subsequent research by Hubler (2011) and Sbia et al (2014) Their findings suggest that increased trade openness can enhance energy efficiency over time, leading to significant power conservation, which may help alleviate import tariffs and bolster foreign companies.
Reducing energy intensity is a key strategy for achieving energy conservation A study by Mahmood and Ahmad in 2018 highlighted the relationship between energy intensity and trade openness, indicating that managing energy consumption is crucial, particularly in the context of high energy prices and increasing taxes.
Research indicates a significant negative relationship between energy intensity levels and trade openness Shen (2007) found that as trade openness increases, energy intensity tends to decrease, as the energy savings from imports surpass the energy used in exports, ultimately contributing positively to GDP per capita.
The ASEAN Plan of Action for Energy Cooperation (APAEC) has set ambitious regional targets to reduce energy intensity by 20% by 2020 and by 32% by 2025 The organization believes that embracing energy-efficient technologies will enhance trade activities in the region, enabling better adaptation to global market competition.
Gross fixed capital formation (GFCF) is a key macroeconomic indicator featured in national accounts, including the United Nations System of National Accounts (UNSNA) and the European System of Accounts (ESA) It quantifies the value of new or existing fixed asset acquisitions by businesses, governments, and households (excluding unincorporated enterprises), minus the disposals of these assets As a vital component of gross domestic product (GDP) expenditure, GFCF reflects the portion of new value added to the economy that is reinvested rather than consumed.
Capital formation increases the flow of money within the economy by accumulating capital goods, which drives investment and enhances the production of goods and services This process not only boosts the income of the population but also stimulates demand, contributing to overall economic growth.
The quality of a country's domestic infrastructure significantly influences its trade propensity Jansen and Nordås (2004) employed ordinary least squares to examine the relationship between institutions and trade openness in both developing and developed nations, while also considering domestic trade policies and infrastructure Their findings highlight that robust domestic infrastructure plays a crucial role in enhancing trade openness.
Y A., Osei, D B & Ibrahim, M (2019) examined the determinants of international trade measured by openness and exports in Africa relying on data for 46 countries over the period 1980–2015 Domestic savings, inflation and gross fixed capital formation are robust determinants of trade openness for lower-income countries However, for lower–middle- income countries, only capital formation significantly spurs integration with international markets.
The labor force, as defined by the World Bank, includes individuals aged 15 and older who are either employed or actively seeking employment, contributing to the production of goods and services While previous research has primarily focused on how trade affects the labor force, this study investigates the reverse relationship, specifically examining whether the total labor force influences trade openness Tahir et al (2018) conducted an analysis of the macroeconomic factors affecting trade openness in SAARC countries from 1971 to 2011, finding that both the size of the labor force and currency exchange rates have a negative impact on trade openness.
Urbanization refers to the concentration of people into discrete areas (US EPA) In other words, it is the increase in the number of people in towns and cities.
Research hypotheses
After referring from theoretical and empirical review, we can hypothesize for the topics impacts of renewable energy on trade openness of ASEAN countries from 2007 to 2021, in details:
1: The percentage of renewable energy used and trading openness have a positive relationship
2: The energy intensity level and trading openness have a negative relationship. 3: The gross fixed capital formation per capita and trading openness have a positive relationship.
4: The total labor force and trading openness have a negative relationship.
5: The percentage of urbanization and trading openness have a positive relationship.
METHODOLOGY, MODEL
Research methodology
This research utilizes reliable secondary panel data sourced from the World Bank, encompassing key indicators such as trade openness over the years, renewable energy consumption percentage, energy intensity levels, gross fixed capital formation per capita, urbanization rates, and total labor force The analysis focuses on 11 ASEAN countries, including Indonesia, Malaysia, Singapore, Thailand, Vietnam, the Philippines, Brunei, Myanmar, Timor Leste, Laos, and Cambodia.
This study utilizes Excel and Stata software to summarize and process data, employing OLS regression to model the statistical relationship between trade openness and several independent variables, including renewable energy consumption, energy intensity level, gross fixed capital formation per capita, urbanization rate, and total labor force The analysis covers the period from 2007 to 2021.
Our analysis, grounded in prior studies and the distinctive characteristics of the data, has led to the development of an Ordinary Least Squares (OLS) model that illustrates the relationship between key variables, specifically trade openness as a function of renewable energy (REN), energy intensity (EIN), urbanization (URB), total labor force (TLF), and gross fixed capital (GFC).
TLF: Total Labor Force (million people)
GFC: Gross Fixed Capital Formation Per Capita (current $US/person)
Trade openness is influenced by various factors, including the unemployment rate, inflation, and net exports To account for additional variables not included in our analysis, we introduce a disturbance term, denoted as u Consequently, we establish a population regression model (PRM) to better understand these relationships.
In the case of this study, the PRM can be rewritten as:
TRO = β + β REN + β EIN + β URB + β TLF + β GFC+ u 0 1 2 3 4 5 i
The model includes an intercept term (β0) and several coefficients: β1 for renewable energy consumption (RENi), β2 for energy intensity level (EINi), β3 for urbanization rate (URBi), β4 for total labor force (TLFi), and β5 for gross fixed capital formation per capita (GFCi) Additionally, the disturbance term (ui) accounts for other unlisted variables that may influence trading openness.
From the population regression model, we concluded to a final sample regression model (SRM):
TRO = β + β REN + β EIN + β URB +β TLF + β 0 1 2 3 4 5 GFC+u i
Table 2.1 Regression coefficient expected sign
Variable Name Unit Regression coefficient expected sign
TLF Total Labor Force People -
GFC Gross Fixed Capital Per
Data inferences
The dataset was processed using Stata, which provided key statistical outputs including the number of observations (Obs), the average value (Mean), the standard deviation (SD), and the minimum (Min) and maximum (Max) values, as detailed in the table below.
Figure 2.2 Statistical description of variables (Source: STATA)
The TRO variable comprises 157 observations, with a mean trade openness of 123.86% and a standard deviation of 84.68%, indicating moderate variation among countries The minimum value of 11.86% and maximum value of 437.33% highlight a significant disparity in trade openness across nations.
The REN variable consists of 143 observations, exhibiting a mean of 31.10% and a standard deviation of 25.08%, indicating significant variability in its values The minimum value is 0%, highlighting that some regions have not accessed green energy, while the maximum value reaches 85.71%, reflecting a high level of usage compared to the global average of 29% reported by the Center for Climate and Energy Solutions.
The EIN variable consists of 143 observations, indicating potential missing values from less developed countries or those experiencing civil conflict The mean value is 3.07%, with a standard deviation of 1.2%, a maximum of 6.35%, and a minimum of 1.33%, reflecting a slight variation in the data.
The TLF variable has 164 observations, with the mean of 28300000 people and std of
35600000 people showing a large gap among the labor force in 11 nations The min value is
The significant disparity between a maximum GDP of 136,000,000 and a value of 183,516 highlights the impact of country size and population on economic metrics While a larger population can influence the overall GDP, it may not substantially affect the GDP per capita, illustrating the complexities of economic comparisons across nations.
The GFC variable consists of 158 observations, revealing a mean of 2994.346 and a standard deviation of 4705.933, indicating significant capital disparity across 11 nations The minimum value recorded is 98.5717, while the maximum reaches 17341.54, highlighting a substantial gap influenced by the varying levels of development among these countries.
Using STATA software to run the command cor trade openness REN EIN URB TLF GFC (obs7) to analyze the correlation between the variables we obtained the results as follows:
Figure 2.3 Correlation between variables (Source: STATA)
A recent analysis reveals that three out of five independent variables exhibit a strong correlation with the dependent variable of trade openness, indicating their significant influence However, energy intensity level shows a weak correlation of -0.0415, while the total labor force demonstrates a moderate correlation of -0.3288 with trade openness.
The correlation analysis reveals varying relationships between gross domestic product per capita and several key variables A moderate negative correlation of r (TRO, REN) = -0.4668 indicates that as renewable energy consumption increases, GDP per capita tends to decrease Similarly, a weak negative correlation of r (TRO, EIN) = -0.1310 suggests a slight inverse relationship between GDP per capita and energy intensity levels In contrast, a strong positive correlation of r (TRO, URB) = 0.6368 shows that higher urbanization rates are associated with increased GDP per capita Additionally, a moderate negative correlation of r (TRO, TLF) = -0.3615 indicates a negative relationship between GDP per capita and the total labor force, while a strong positive correlation of r (TRO, GFC) = 0.6753 highlights the positive association between GDP per capita and gross fixed capital per capita.
The correlation analysis reveals various relationships between independent variables The correlation between REN and EIN is weak and negative (r = -0.0796), indicating an opposite direction Conversely, REN and URB exhibit a very strong negative correlation (r = -0.8113), also in the opposite direction A weak positive correlation exists between REN and TLF (r = 0.0645), while REN and GFC show a strong negative correlation (r = -0.6269) The relationship between URB and EIN is weak and positive (r = 0.0339), whereas URB and TLF have a weak negative correlation (r = -0.1108) However, URB and GFC demonstrate a very strong positive correlation (r = 0.8414) The correlation between EIN and TLF is weak and positive (r = 0.0700), while EIN and GFC show a weak negative correlation (r = -0.0185) Lastly, TLF and GFC exhibit a moderate negative correlation (r = -0.3207).
CHAPTER 3 ESTIMATED REGRESSION MODEL AND STATISTICAL INFERENCES
Estimated model results
Figure 3.4 Estimated model result (Source: STATA)
On the previous Sector, the team concluded the Sample Regression Model
According to the estimated result from STATA using OLS method (Ordinary Least Squares method), we obtained the Sample Regression Model as below
The coefficient of determination, R-squared, is 0.5296, indicating that 52.96% of the total variation in trade openness is explained by independent variables such as renewable energy consumption, energy intensity level, gross fixed capital formation per capita, urbanization rate, and total labor force, while the remaining variation is attributed to other factors.
Adj R-squared = 0.5117, shows a quite similar result to R 2
Table 3.2 Meaning of estimated coefficients
= 72.45 The value of trade openness when all independent variables equal to zero.
Under the condition that other variables are constant, when renewable energy consumption increases by 1%, the trade openness will increase by 0.234%
Under the condition that other variables are constant, when energy intensity level increases by 1%, the trade openness will decrease by 8.435%
Under the condition that other variables are constant, when the urbanization rate increases by 1%, the trade openness will increase by 1.5875%
Under the condition that other variables are constant, when total labor force increases by 1 person, the trade openness will decrease by 0.0000259%
Under the condition that other variables are constant, when the gross fixed capital per capita increases by 1 $US/person, the trade openness will increase by 0.005%
Hypothesis testing
3.2.1 Statistical significance of individual coefficients
To test the significance of estimated coefficients of estimators we construct the following hypothesis testing, considering a significance level of 10%.
3.2.1.1 Percentage of renewable energy consumption
Two-tailed test: Does the percentage of renewable energy consumption actually affect trading openness?
P-value(ts) = 0.526 > 0.1 → fail to reject H 0
Conclusion: The percentage of renewable energy consumption does not have an effect on trading openness at a significance level of 10%.
Two-tailed test: Does the energy intensity level actually affect trading openness?
Conclusion: Energy intensity level actually has an effect on trading openness at a significance level of 10%.
Two-tailed test: Does the urbanization rate actually affect trading openness?
Conclusion: Urbanization rate actually has an effect on trading openness at a significance level of 10%.
Two-tailed test: Does total labor force actually affect trading openness?
Conclusion: The total labor force actually has an effect on trading openness at a significance level of 10%.
3.2.1.5 Gross fixed capital per capita
Two-tailed test: Does gross fixed capital per capita actually affect trading openness?
Conclusion: Gross fixed capital per capita actually has an effect on trading openness at a significance level of 10%.
3.2.2 Overall significance of the regression model
To test the overall significance of the regression model, we construct the following hypothesis testing:
From the estimation output we have F = 29.50 s
At 5% level of significance P-value (F ) < α → reject the null hypothesis H s 0
Conclusion: The model is statistically significant at 5% significance level
3.2.3 Theoretical and expected fitting test
To test the fitness of estimators with expected sign, we construct the following hypotheses testing, considering a significance level of 10%
Testing reveals that the coefficients of all independent variables significantly influence the dependent variable, aligning with expected signs This indicates that the relationships between the dependent variable and the independent variables are consistent with established economic theories and prior research Additionally, the consumption of renewable energy does not significantly impact the trade openness of ASEAN countries during this time period.
Mechanism relationship
The test results indicate that the percentage of renewable energy consumption in 2007-
2021 does not necessarily affect the output of trade openness in ASEAN countries Even
While there is no direct impact of renewable energy consumption on trading percentages, there is a clear connection between other influencing factors and trading data The trend of investing in renewable energy among the 11 studied countries affects their export and import activities Although we do not observe a causative link from renewable energy consumption to trading, we identify a long-term causality from other tested factors to trading variables Consequently, increased renewable energy consumption positively influences the trading market over the long run.
In today's dynamic market, trade openness drives firms to innovate and adapt by adopting energy-efficient technologies Advances in transportation and production have reduced distances between urban areas, enhancing infrastructure and lowering energy consumption for goods and services transport Additionally, many OECD countries have transitioned from energy-intensive manufacturing to less energy-intensive service economies, further contributing to this trend.
Gross fixed capital formation significantly boosts economic growth and trade by creating substantial benefits, increasing investments, and expanding markets through economies of scale It facilitates the transfer of information, technology, and knowledge spillovers, leading to better resource utilization and technological advancements in trade This process enhances foreign exchange earnings, which can be reinvested into underdeveloped sectors of the economy Numerous theorists support this concept, with studies indicating that human and physical capital play a crucial role in less developed countries South Asia exemplifies a region where economic weakness persists, relying heavily on labor capital to drive rapid economic growth.
The ASEAN labor force has become the third largest globally and continues to grow, with projections indicating that the total number of workers in these countries will reach 385 million by 2030, according to Statista However, the workforce is unevenly distributed across various levels of development among ASEAN nations, with some economies being more efficiency-driven than others, as noted by ASEAN Briefing.
ASEAN countries, including Indonesia, Vietnam, Thailand, and the Philippines, benefit from competitively priced labor and essential regulatory frameworks, enabling them to engage in the manufacturing of complex goods and offer limited value-added services In contrast, factor-driven economies like Cambodia, which face challenges such as low education levels and income, primarily focus on basic manufacturing services and components, often serving as extensions of more sophisticated production lines from nations like China.
Innovation-driven economies such as Malaysia and Singapore excel in high-value manufacturing, professional services, and the assembly of complex components However, these countries face higher human resource costs and a relatively smaller labor force compared to others.
Most export products from ASEAN nations have limited value addition, while a large labor force enhances the production of goods and services in both quantity and diversity This increase in production can lower the final product prices, potentially reducing reliance on imported goods due to tariffs and protectionist policies.
The result shows a positive impact of urbanization rate on trade openness From ASEAN scenario, we have found out some incentives for that:
Urban areas host a significant number of modern industrial plants, providing workers with superior machinery and tools compared to their rural counterparts This access enhances their skills and productivity, leading to the production of complex, high-value goods primarily in urban settings Consequently, the need for imported materials and patterns from other countries rises to support this manufacturing process.
In Vietnam, numerous local companies in emerging urban areas are engaged in assembling Samsung smartphones for export; however, they rely on importing essential components from countries such as Korea and Thailand This assembly process contributes to a rise in both Vietnam's export and import statistics.
Recommendations
The regression analysis indicates that the utilization of renewable energy has minimal influence on the trading activities and GDP of ASEAN countries Consequently, it is essential for governments and corporations in these nations to recognize this finding.
Green energy plays a crucial role in promoting sustainable economic development by addressing global economic polarization Implementing effective green energy policies can stimulate job creation, enhance energy security, and reduce reliance on fossil fuels Additionally, transitioning to renewable energy sources helps mitigate climate change, improves public health, and fosters innovation By prioritizing green energy initiatives, countries can achieve greater economic equity, enhance resilience against economic fluctuations, and contribute to a more sustainable future for all.
Following the suggestions from previous studies, our team highlighted some viable recommendations for Southeast Asian countries to enact:
Here is a rewritten paragraph that captures the essence of your original content, optimized for SEO:"To mitigate the growing demand for electric power, countries should implement cooperative subsidies for green energy programs and develop new power generation capacity By integrating green power into their manufacturing processes, nations can reduce their reliance on foreign oil and fossil fuels, a strategy that has become increasingly crucial in the wake of the Russia-Ukraine war and US-China trade tensions, which have had significant economic implications."
As public awareness grows, the ASEAN community can emphasize that renewable energy has the potential to attract green-power investors However, many ASEAN nations are still transitioning to renewable energy, which may delay the establishment of trust with foreign investors, particularly from the EU To address this, these countries must develop trading policies that focus on regional cooperation and building credibility A commitment to boost the share of green power from 13% to 23% by 2025 has been officially recognized.
In 2015, the 10 member nations of the ASEAN community recognized the potential for enhanced regional integration, which aimed to improve their global leadership ranking and accelerate trade with prospective suppliers.
Enhancing forecasting tools and adopting new technologies are crucial for Thailand's Energy 4.0 initiative, which aims to provide energy access to 99% of the population and expand transmission interconnections throughout Southeast Asia Thailand has established itself as a leader in green energy within the region, fostering market competitiveness among ASEAN nations This strategic approach is anticipated to promote inclusivity and facilitate more efficient, low-carbon trade among countries.
This research investigates the impact of renewable energy on trade openness among ASEAN countries from 2007 to 2021, aiming to foster green energy economies through effective policy-making Utilizing the OLS model, our analysis encompasses all Southeast Asian nations, revealing significant insights into the relationship between renewable energy and trade dynamics during this period.
Over the past 14 years, many ASEAN countries have steadily decreased their renewable energy consumption, yet this trend has continued to foster long-term foreign trade Additionally, various factors such as energy intensity levels, urbanization rates, total labor force, and gross fixed capital per capita have proven to be valuable metrics for evaluating trade openness in the region.
To promote international trade, it is essential to foster regional coordination to build external trust, alongside implementing subsidy projects for green power facility development Most importantly, advancing technologies and improving forecasting capabilities are crucial for ensuring seamless grid integration.
Despite the challenges we faced in sourcing relevant articles and interpreting our findings, our team has worked diligently to produce a high-quality report within a limited timeframe We extend our heartfelt thanks to Dr Nguyễn Bình Dương for her invaluable guidance throughout the course, and we look forward to her feedback to enhance our essay further.
1 Adom, P.K (2015) “Asymmetric impacts of the determinants of energy intensity in Nigeria,” Energy Economics, 49, pp 570–580 Available at: https://doi.org/10.1016/j.eneco.2015.03.027.
2 ASEAN Centre for Energy (2021) “ASEAN Regional Product Registration System,” https://united4efficiency.org/, 24 August Available at: https://united4efficiency.org/wp- content/uploads/2021/08/ASEAN-PRS_Basic-Direction-and-Benefits_Rio-Jon-Piter- Silitonga.pdf (Accessed: March 17, 2023).
3 Ben Jebli, M and Ben Youssef, S (2015) “Output, renewable and non-renewable energy consumption and international trade: Evidence from a panel of 69 countries,” Renewable Energy, 83, pp 799–808 Available at: https://doi.org/10.1016/j.renene.2015.04.061.
4 Deichmann, U., Reuter, A., Vollmer, S and Zhang, F (2019) The relationship between energy intensity and economic growth: New evidence from a multi-country multi-sectorial dataset World Development, 124, p.104664.
5 Energy intensity indicators (no date) Energy.gov Available at: https://www.energy.gov/eere/analysis/energy-intensity-indicators (Accessed: March 17, 2023).
6 Global energy intensity continues to decline (no date) Homepage - U.S Energy Information Administration (EIA) Available at: https://www.eia.gov/todayinenergy/detail.php?id'032 (Accessed: March 17, 2023).
7 Industrial policy and renewable energy: Trade conflicts (no date) Available at: https://digitalcommons.cwu.edu/cgi/viewcontent.cgi?article29&context=cobfac (Accessed: March 17, 2023).
8 Kittner, N et al (2022) ASEAN needs to work together on Green Energy, East Asia Forum Available at: https://www.eastasiaforum.org/2022/06/21/asean-needs-to-work- together-on-green-energy/ (Accessed: March 17, 2023).
9 Low- and middle-income countries, by region and subregion (2022) Guttmacher Institute Available at: https://www.guttmacher.org/regional-and-subregional-country- classifications (Accessed: March 17, 2023).
10 Moxa helps Thailand achieve energy transition and realize its goal of becoming a Sustainable Power Hub (no date) Moxa Available at: https://www.moxa.com/en/about- us/news-events/news/2022/moxa-helps-thailand-achieve-energy-transition (Accessed: March
11 Osei, Dennis Boahene; Sare, Yakubu Awudu; Ibrahim, Muazu (2019): On the determinants of trade openness in low- and lower-middle-income countries in Africa: How important is economic growth?, Future Business Journal, ISSN 2314-7210, Springer, Heidelberg, Vol 5, Iss 1, pp 1-10, https://doi.org/10.1186/s43093-019-0002-8
12 Renewable energy – powering a safer future (no date) United Nations United Nations Available at: https://www.un.org/en/climatechange/raising-ambition/renewable-energy (Accessed: March 17, 2023).
13 Trade openness (2016) United Nations Economic and Social Commission for Western Asia Available at: https://archive.unescwa.org/trade-openness-0 (Accessed: March 17, 2023).
14 Vakulchuk, R., Overland, I and Suryadi, B (2022) “ASEAN’s energy transition: How to attract more investment in renewable energy,” Energy, Ecology and Environment, 8(1), pp 1–16 Available at: https://doi.org/10.1007/s40974-022-00261-6.
Recommended for you kinh t ế l ượ ng
Tổng hợp đề CK KTL đáp án - đ ề thi t ổ n… kinh tế lượng 100% (8)
17 Đ Ề Kinh Te Luong TEST1 kinh tế lượng 100% (6)
9 Ý NGHĨA B Ả NG H Ồ I QUY MÔ HÌNH BẰN… kinh tế lượng 100% (5)
Tiểu luận Kinh tế l ượ ng - nhóm 11-đã… kinh tế lượng 100% (5)