How Does a Green Economy Impact Climate Change?

A green economy helps the environment by using renewable energy and reducing carbon emissions. It saves natural resources and supports sustainable practices. This type of economy creates eco-friendly jobs and supports low-carbon technology. 

By focusing on the environment and the economy together, a green economy builds a healthier world and a stronger future.

What is the relationship between the green economy and climate change?

A few years ago, the economy depended heavily on natural resources, but now there is a crisis with shortages and rising costs. To grow without harming the environment, we need green economy models and better resource management.

A green economy uses less carbon, saves resources, and includes everyone in the process. Both public and private investments are needed to cut emissions, reduce pollution, and use energy more efficiently. Medical science and the green economy intersect to promote healthier lives and sustainable practices through eco-friendly innovations in healthcare.

For young people, education and skills training are essential. This helps them support green jobs and create eco-friendly solutions.

In Bosnia and Herzegovina, green efforts are still small because the sector is underdeveloped. This program aims to protect nature and the economy from climate change, improve energy efficiency, support green energy, and build a low-emission society.

Primary activities in the Green Economy and Climate Change program include:

• Supporting sustainable economic growth through low-carbon and energy-efficient use of resources.
  
• Promoting green finance, technologies, and investments.
  
• Helping young people develop skills and ideas to support the green economy.
  
• Improving data and skills to understand and adapt to climate change impacts.
  
• Creating methods to help decision-makers prepare effective climate policies.
  
• Promoting education, training, and public awareness on climate change and international cooperation, including the Paris Agreement.
  
• Working with different groups to reach more people and have a bigger impact.

How Green Technology and Renewable Energy Bring Green Economic Growth?

This study looks at how green technological innovation (GTI) and ICT technologies affect carbon dioxide (CO2) emissions. It focuses on selected emerging economies from 1990 to 2020. The study also considers the roles of renewable energy (RNE), economic growth (GDP), and structural changes in these countries.

To analyze the data, the researchers used advanced methods like CUP-FM, CUP-BC, and panel quantile regression. These tools help ensure accurate results by accounting for differences between countries.

The findings show that green technological innovation, renewable energy, and structural transformation contribute to lowering CO2 emissions. Green technologies improve energy efficiency and support goals like carbon peaking and carbon neutrality.

However, economic growth (GDP) is connected to higher CO2 emissions. This means that when economies grow, emissions may rise at first. But with better technologies and smart policies, this effect can be reduced.

The study suggests that offering fiscal incentives, investing in GTI, using more renewable energy, and applying ICT effectively can help lower CO2 emissions in emerging economies.

GTI and Environmental Sustainability

In recent years, green technological innovations (GTIs) have gained a lot of attention for their ability to protect the environment. GTIs are technologies designed to reduce environmental harm and support sustainable development. Many studies have explored how GTIs affect different areas. For example, increasing renewable energy sources such as solar and wind helps reduce greenhouse gas emissions and enhances air quality.

However, some research shows that the overall effect of renewable energy on energy output and cutting emissions may not be very strong. GTIs also face challenges such as high costs, a lack of political support, and strict regulations.

Despite these challenges, advancements in renewable energy technology can reduce its negative environmental effects. Efficient GTIs can bring economic benefits by lowering pollution and saving resources. 

Adopting GTIs can significantly reduce environmental damage. Some studies also suggest that in lower-income countries, technological innovation might still lead to more pollution. However, a study of EU-27 countries (1992–2014) found that environmental innovation did help reduce carbon emissions, while general innovation did not.

RNE and Environmental Sustainability

Renewable energy (RNE) and environmental sustainability have gained growing attention in research. Many studies show that RNE sources, like solar and wind, are less harmful to the environment compared to fossil fuels.

Some researchers have compared different RNE policies, such as feed-in tariffs and renewable portfolio standards. These studies agree that traditional energy sources harm the environment, while RNE can reduce pollution and improve energy systems.

RNE is seen as a good option for improving environmental quality and supporting economic growth. RNE highlighted the long-term role of energy in reducing environmental damage. RNE a two-way link between energy use and the environment. RNE positively impacts the environment when economic factors are included.

Overall, research on RNE and the environment shows mixed but mostly positive results. Continued studies are needed to better understand how RNE affects the environment and to guide future energy policies.

ICT Technologies and Environmental Sustainability

There is a lot of research on how information and communication technologies (ICT) affect environmental sustainability. Some studies show that ICT like data centers and electronic devices can harm the environment by using a lot of energy and producing CO2. In fact, ICT may cause more emissions than the aviation industry. The production of ICT devices and poor e-waste management also add to environmental problems.

However, ICT can also help the environment. For example, it can reduce the need for travel, lower emissions, and help manage resources like water and energy more efficiently. But there are concerns about “rebound effects,” where the benefits of ICT are reduced by increased energy use or the creation of new services that consume more resources.

In general, ICT impacts the environment in both beneficial and harmful ways. While green technologies and renewable energy are widely seen as helpful in reducing carbon emissions, there is still not enough research on how these technologies work together in emerging economies. This gap needs to be filled to better guide sustainable development efforts and policymaking.

Model Specification, Data, and Research Methodology

Theoretical Framework and Data Descriptions:

This study looks at how green technology (GTI), ICT, and renewable energy (RNE) affect CO2 emissions in selected emerging economies from 1990 to 2020. CO2 emissions (measured in tons per person) are used to represent environmental quality.

GTI is measured by the ratio of environment-related patents (data from OECD). The ICT index includes mobile users, internet use, broadband, and landline subscriptions. These ICT data, along with CO2, RNE, and energy use, are taken from the World Bank.

To avoid overlapping data issues, the ICT index was created using principal component analysis (PCA). RNE is measured as a percentage of total energy, and energy use is measured in kilowatt-hours per person.

The study also includes GDP and structural change (measured by service exports) as control variables, both from the World Bank. All these indicators help explore how technology and energy affect environmental sustainability and economic growth.

Econometrics Model :

This study looks at how green innovations (GTIs), ICT technologies, and renewable energy (RNE) affect CO₂ emissions in emerging economies. Research suggests that GTIs help protect the environment by cutting emissions, improving waste management, saving resources, and supporting smart technologies. 

However, more analysis is needed to understand how GTIs and ICT contribute to sustainable and inclusive growth. To do this, the study uses a model that focuses on how these factors impact CO₂ emissions and overall environmental quality.

  (1)

To normalize the data series, each variable is converted into a natural logarithm, and the elasticities of the regression coefficients are used to describe the data. To avoid omitted variable bias, GDP and STC are included as control variables. Consequently, Equation (2) represents the log-linear form of Equation (1).

(2)

In this equation, GDP, RNE, ICT, GTI, and STC represent economic growth, renewable energy, information and communication technology, green technology innovation, and structural transformation, respectively. The slope coefficients, denoted by β1 to β5, measure the relationship between each variable and CO₂ emissions. The subscript (i) indicates the cross-sectional units (countries 1 to 11), and the period spans from 1990 to 2020. β0 is the intercept, and μ stands for the random error term.            

Estimation Techniques

Testing Cross-Sectional Dependence (CSD)

In panel data analysis, checking for cross-sectional dependence (CSD) and spatial correlation heterogeneity (SCH) is important to get accurate results and choose the right method. This study used the Lagrange Multiplier test to check for CSD, especially since the data has more periods (T) than countries (N). The test calculates statistics to see if CSD is present or not.

(3)

(4)

The term ρij² represents the correlation between countries. To check for cross-sectional dependence (CSD) in the model’s errors, the study used tests by Pesaran, Frees, and Friedman. The countries in the study may differ in things like economy, energy use, population growth, and anti-corruption efforts. To handle these differences, a slope homogeneity test was used, which checks if the relationships are the same across countries while considering their unique features.

(5)

(6)

In this equation, two statistics show slope homogeneity and bias-adjusted slope homogeneity. If CSD and SCH are found, advanced econometric methods must be used in the next steps.  

Second-Generation Unit Root Tests

This research applied two advanced stationarity tests: the cross-sectional augmented Dickey–Fuller (CADF) and the cross-sectional Im–Pesaran–Shin (CIPS) methods. These tests are better than traditional ones because they consider differences between countries. They help check if the data series is nonstationary (changing over time) in a more accurate way.

(7)

(8)

where is the cross-sectional average of the lagged variable, and is the cross-sectional mean of the first difference variable, ti(N, T) = CADF, representing the estimated OLS regression value for the ith cross-section.

Panel Cointegration Test

The panel cointegration test checks if there is a long-term relationship between the variables in the model. This study used Westerlund’s second-generation cointegration test, which is designed to handle differences and connections between countries. It focuses on how quickly errors are corrected over time, helping provide more reliable results for emerging economies.

(9)

Here in this context, the deterministic component is denoted by and the error correction term by dt and ðt, respectively. To evaluate the null hypothesis of no cointegration, we use four different statistical methods: the first two for group statistics (Gt and Ga) and the remaining two for panel statistics (Pt and Pa). The test equations are as follows:

(10)

(11)

(12)

(13)

CUP-FM and CUP-BC:

For estimating long-term relationships, this study uses CUP-FM and CUP-BC methods developed by Bai, Kao, and Ng. These methods give accurate and reliable results. CUP-FM enhances precision by regularly refining the data, whereas CUP-BC corrects estimation bias. Together, these methods ensure reliable and robust outcomes.

(14)

Here, ΔFεi and Δμεi show one-sided covariance estimates. When variables have mixed levels of integration, the CUP-FM and CUP-BC methods still give strong and unbiased results. These methods are very effective for long-term analysis and are widely used in similar studies.

However, CUP-FM and CUP-BC do not fully capture the different effects of growth, green technology, political risks, and policy uncertainty on the environment. To better understand how energy use and carbon emissions vary across different conditions, the study also uses panel quantile regression.

Results and Discussion: Impact of green technology and energy on green economic growth

The results in Table 1 show strong evidence of cross-country dependence (CSD) and slope heterogeneity (SCH). The low p-values (at the 1% level) from the LMBP, SLMBC, Pesaran, Frees, and Friedman tests confirm that the countries in the study are not independent, meaning changes in one country can affect others. The SCH test also rejects the idea that all countries share the same slope coefficients. This means the effects of GDP, RNE, ICT, GTI, and STC differ across the emerging economies in the sample.

Findings from the CSD and SCH tests.

Variables	LmEP	SLMp	SLMp	CDp
LogCO2	255.7968 ∗	19.14523 ∗	19.99201 ∗	55.3362 ∗
LogGDP	592.2352 ∗	51.22336 ∗	51.6950 ∗	16.74955 ∗
LogRNE	252.0207 ∗	18.78519 ∗	18.5840 ∗	8.9607 ∗
LogICT	362.4773 ∗	29.31681 ∗	29.3654 ∗	10.20462 ∗
LogGTI	100.7474 ∗	43.61840 ∗	43.2547 ∗	13.41912 ∗
LogSTC	1088.299 ∗	98.52118 ∗	98.4741 ∗	32.10795 ∗
SCH test	–	–	–	–
Tests	∆	–	–	Adj. ∆
	13.150 ∗	–	–	14.945 ∗

Note: CSD H0 is cross-sectional sovereignty. Abbreviations: CSD, cross-sectional support; SCH, spatial correlation heterogeneity. ∗Indicates p  < 0.01.

Table 2 shows the CIPS and CADF test results. The findings suggest that all variables are non-stationary at level [I(0)], meaning they have unit root issues. However, after taking the first difference [I(1)], the variables become stationary, indicating strong statistical significance at this stage.

Unit root analysis CIPS and CADF.

	CIPS Level	1st difference	CADF Level	1st difference
LogCO2	−1.291	−19.951 ∗∗∗	−1.448	−8.741 ∗∗∗
LogGDP	−1.727	−20.759 ∗∗∗	−2.000	−8.741 ∗∗∗
LogRNE	−1.346	−11.789 ∗∗∗	−1.155	−15.673 ∗∗∗
LogICT	−1.425	−9.369 ∗∗∗	−1.079	−17.915 ∗∗∗
LogGTI	−3.970	−21.550 ∗∗∗	−2.491	−11.587 ∗∗∗
LogSTC	−2.644	−11.748 ∗∗∗	−3.556	−9.372 ∗∗∗

Note: H0 is a unit root. Abbreviations: CADF, cross-sectional augmented. Dickey–Fuller; CIPS, cross-sectional Im–Pesaran–Shin. Level of significance at  ∗∗∗ = 10%.

Tables 3 and 4 show the cointegration test results. The findings confirm a strong long-term relationship between LogGDP, LogRNE, LogICT, LogGTI, and LogSTC. The significance of Gt, Ga, Pt, and Pa at the 1% level supports cointegration. This enables the application of CUP-FM and CUP-BC techniques to calculate long-term impacts.

Cointegration test.

	No shift Statistic	p-Value	Mean shiftStatistic	p-Value	Regime shift Statistic	p-Value
LMτ	−7.584 ∗	0.001	−8.674 ∗	0.001	−6.516 ∗	0.001
LMθ	−8.101 ∗	0.001	−8.645 ∗	0.001	−5.779 ∗	0.001

Note: H0 is no cointegration. ∗Indicates p  < 0.01.

Westerlund panel cointegration tests

Statistics	Value	Z-value	p-Value
Gt	−1.743 ∗	1.484	0.009
Ga	−1.799 ∗	4.287	0.001
Pt	−5.138 ∗	0.902	0.000
Pa	−2.121 ∗	2.465	0.000

Note: H0 is no cointegration, ∗indicating p  < 0.01. 

Table 5 indicates that rising economic growth (GDP) leads to higher CO2 emissions. This supports the idea that in the early stages of growth, more energy use leads to higher emissions. 

However, in the later stages, the adoption of cleaner and more efficient technologies contributes to lowering emissions. As economies grow and shift to services, energy use and emissions may level off or decline. Economic growth also promotes innovation to lower or capture CO2. Similar findings are reported in studies [26, 62–65].

PQR results

Variables quantile	0.05	0.1	0.2	0.3	0.4	0.5	0.6	0.7	0.8	0.9	0.95
LogGDP	0.460 ∗∗	0.454 ∗	0.166 ∗	−0.340	−0.407 ∗	−0.522 ∗	−0.560 ∗	−0.437 ∗	−0.420 ∗	−0.452 ∗∗	−1.009 ∗
LogRNE	−0.795	−0.820 ∗	−0.786 ∗	−0.658 ∗	−0.506-	−0.483 ∗	−0.469 ∗	−0.403 ∗	−0.478	−0.446 ∗	−0.351 ∗∗
LogICT	−0.588	−0.656 ∗	−0.549	−0.317 ∗∗∗	0.172 ∗	−0.063	−0.016	0.036	0.010	0.056 ∗∗∗	−0.190
ILogGT	0.012	−0.007	0.076 ∗	0.348 ∗∗∗	0.214 ∗∗∗	0.215 ∗∗	0.243 ∗	0.221 ∗	0.166	0.186	0.183
LogSTC	−0.040 ∗	−0.015	0.032 ∗∗	0.091 ∗∗∗	0.095 ∗	0.114 ∗	0.119	0.069 ∗∗	0.048	0.014	0.080

•  ∗∗∗,  ∗∗, and  ∗ designate significance at the 1%, 5%, and 10%, respectively.

First of all, the fixed effects panel quantile analysis using the method of moments shows that green technological innovations (GTIs) have a small positive effect on environmental quality at the 0.05, 0.1, 0.8, and 0.95 quantiles. However, at other quantiles, the effect of GTIs is stronger and statistically significant. These findings align with the results received from the CUP-FM and CUP-BC methods.

Secondly, GTIs help improve environmental sustainability. They fulfill energy demands and play a role in manufacturing activities. GTIs also help reduce poverty and create jobs, especially in rural areas. This may explain their positive impact on the environment in emerging economies.

More importantly, the effect of GTIs on reducing CO2 emissions becomes stronger in higher-income countries. This is likely because wealthier countries have better science, technology, and skilled labor. They also invest more in modern GTIs. These investments help improve both the economy and the environment in emerging economies.

What Is The Singapore Green Economy Plan 2030?

The Singapore Green Plan 2030 is a national plan to make Singapore more sustainable and fight climate change. It was launched in February 2021. The plan specifies clear goals to help Singapore become a greener and cleaner country. It focuses on five main areas. These areas guide how Singapore will grow in a way that protects the environment. The plan is led by the National Climate Change Secretariat and other state agencies.

City in Nature:

This pillar focuses on enhancing Singapore’s natural environment by integrating greenery into urban spaces. Key initiatives include:

• Planting 1 million more trees by 2030.
  
• Increasing the land area by more than 50% compared to 2020 levels.

• Ensuring that every household is within a 10-minute walk from a park.FHA-FnB+2Wikipedia+2Condé Nast Traveler+2
  

Energy Reset:

This pillar aims to cut carbon emissions and promote the use of clean energy by:

• Increasing solar energy deployment to at least 2 gigawatt-peak by 2030, sufficient to power around 350,000 households annually.
  
• Deploying 200 megawatts of energy storage systems beyond 2025 to manage solar intermittency.
  
• Importing up to 6 gigawatts of low-carbon electricity by 2035, accounting for approximately 30% of Singapore’s energy needs.
  
• Transitioning all new car registrations to cleaner-energy models from 2030 and installing up to 60,000 electric vehicle charging points.Jones Day+1Wikipedia+1Wikipedia+1Jones Day+1Wikipedia+3FHA-FnB+3Wikipedia+3
  

Sustainable Living:

This pillar encourages environmentally responsible habits among citizens through:

• Lowering household water use to 130 liters per person each day by 2030.
• Cutting down the amount of waste sent to landfills per person per day by 30% by 2030.
• Expanding the cycling route network to 1,300 km islandwide by 2030.
  
• Ensuring that 75% of peak-hour trips are made via public transport by the 2030s.Singapore Green PlanWikipedia+1Condé Nast Traveler+1
  

Green Economy

To foster economic growth while ensuring environmental sustainability, this pillar includes:

• Developing Jurong Island into a sustainable hub for energy and chemical industries.
• Positioning Singapore as a sustainable tourism destination and a carbon services hub.
  
• Issuing up to S$35 billion in green bonds by 2030 to finance green infrastructure projects.
  
• Developing a green finance taxonomy and collaborating internationally to enhance green finance initiatives.Wikipedia+1Wikipedia+1WSJ
  

Resilient Future

This pillar is dedicated to enhancing Singapore’s ability to adapt to climate challenges by:

• Introducing strategies to safeguard against sea-level rise and extreme weather conditions.
  
• Promoting a circular economy to reduce waste and use resources more efficiently.
  
• Strengthening food security with a goal to meet 30% of the nation’s nutritional needs through local production by 2030. Singapore Green Plan
  

Conclusion

A green economy helps resist climate change by using eco-friendly methods. It lowers greenhouse gas emissions and saves natural resources. 

Using renewable energy and efficient technologies slows down global warming and reduces harm to the environment. A green economy also encourages new ideas, creates jobs, and supports steady economic growth. 

Overall, moving to a green economy is important for protecting the planet and building a healthier future.

FAQs about the Green Economy