Reducing Carbon Emissions from Transport Sector: Experience and Policy Design Considerations

Abstract

Countries aim to reduce fossil fuel usage and related environmental issues through various demand- and supply-side policies. Numerous studies have assessed the policies’ overview. However, analysis of the impacts and effectiveness of these policies in addressing transport-related CO₂ emissions is limited globally and in countries like New Zealand, which have a lower CO₂ emissions energy intensity compared to Europe, Asia, and Oceania averages. Therefore, this study first analyses the trends in energy consumption and CO₂ emissions within the transport sector across the ten largest total CO₂-emitting countries, as well as the ten largest transport CO₂-emitting OECD countries. It then provides a systematic review of the relevant policies and, finally, estimates two econometric models to explore the effects of these policies on the energy market, aimed at reducing GHG emissions globally from the transport sector, with New Zealand as a case study. The study findings indicate that the transport sector remains a significant contributor to global fossil fuel consumption and CO₂ emissions, accounting for 40.4% and 23.3%, respectively, in 2024. The ten largest CO₂-emitting countries—China, the United States, India, Russia, Japan, Germany, South Korea, Iran, Canada, and Saudi Arabia—are responsible for 68% of global emissions. Additionally, the ten OECD countries, except the US, with the highest transport CO₂ emissions—Japan, Germany, South Korea, Canada, Mexico, the UK, Italy, France, Spain, and Australia—accounted for 15.7% of the world’s total transport CO₂ emissions. Although the share of renewable energy and electricity consumption in the transport sector has steadily risen to 3.54% and 1.4%, respectively, in 2022, further adoption of these sources can considerably lower greenhouse gas emissions in this sector. Results also indicate that both demand- and supply-side policies effectively reduce greenhouse gas emissions, with their impact amplified when implemented together. In New Zealand, demand-side policies have proven to be more effective in reducing emissions than supply-side strategies alone, though combining them is the most efficient approach. This study emphasizes the importance of strategic policy implementation to guide the world toward sustainable development.

1. Introduction

Increasing global greenhouse gases and their impact on global warming and human life have increased concern about the future natural threats that may happen and influence humanity, such as long-term droughts, global warming, and floods. The burning of fossil fuels poses significant threats to our environment. Despite heightened global awareness and concern regarding climate change, the demand for fossil fuels and greenhouse gas emissions are on a steady rise, although they are decreasing compared to levels seen in the last century. Many countries continue to struggle with implementing effective and practical environmental policies aimed at decreasing their reliance on fossil fuels [1]. These countries, especially China as the main contributor to the world’s total CO₂ emissions, must make a significant contribution to reducing their greenhouse gas (GHG) emissions and have a greater commitment to international agreements compared to other countries.

Some political decisions will affect the environment dramatically. For example, during Trump’s first presidential term, the United States completely withdrew from the 2015 Paris Agreement, which aimed to mitigate climate change. The consequences of this decision on global warming will be significant, given that the United States ranks as the second-largest CO₂ emitter globally, following China [2]. These countries, especially China, must make a significant effort to reduce their GHG emissions and have a greater commitment to international agreements compared to other countries. A necessary condition for mitigating global warming is the unwavering commitment of nations to international agreements. However, the sufficient condition for success lies in the effective implementation of climate change policies aimed at reducing fossil fuel consumption. These policies are impactful, actively shaping energy consumption patterns and driving down CO₂ emissions [3,4].

IEA reports show that the trend of CO₂ emissions from fuel combustion in the transport sector is increasing [2]. For example, the transport CO₂ emissions decreased by 8.3% from 7737.8 million tonnes in 2015 to 7098.3 million tonnes in 2020 and then increased by 11.9% (7941 million tonnes) in 2022, while its share of total CO₂ emissions has declined (

Table 1. Total CO₂ emissions from fuel combustion by sector (million tonnes of CO₂).

Sector	2015		2020		2022
	Total	Share (%)	Total	Share (%)	Total	Share (%)
World	32,294.2	100.0	31,665.4	100	34,116.8	100
Electricity and heat production	13,540.6	41.9	13,568.2	42.8	14,945.8	43.8
Other energy industry own use	1654.8	5.1	1542.6	4.9	1663.2	4.9
Manuf. Industries and construction	6066.1	18.8	6180.5	19.5	6261.1	18.3
Transport	7737.8	24.0	7098.3	22.4	7941	23.3
Road	5792	17.9	5475	17.3	6022.3	17.6
Other sectors	3294.8	10.2	1935.9	6.1	2717.8	8.0
Residential	1865.9	5.8	773.2	2.4	1938.7	5.7

Source: the author calculated based on the source [2].

). The transport sector accounted for approximately 24% of global CO₂ emissions in 2015, 22% in 2020, and 23.3% in 2022 due to fuel combustion [2]. Reducing emissions in this sector, along with those in the electricity and heat production sectors, can significantly contribute to global climate mitigation efforts. New Zealand’s transport sector is responsible for the majority of total CO₂ emissions, which are steadily rising. In 2020, it represented 45% of total CO₂ emissions (30.8 million tonnes), while, in 2022, it represented 51% of a total of 28.7 million tonnes [2]. This also suggests that reductions in this sector could significantly influence climate mitigation efforts in the country.

On average, the transport sector consumed about 40% of the total fossil fuel consumption in the world during 2000–2022 [5]. Its contribution to the world’s total fossil fuel consumption in 2022 was about 40.4%. The share of renewable energy and electricity consumption in total energy consumption in the transport sector is low. The average shares during 2000–2022 for renewable energy consumption and electricity consumption are 2.1% and 1%, respectively. However, these contributions for 2022 were 3.5% and 1.4%, respectively [5].

Many countries have special strategies to reduce fossil fuel consumption and, consequently, reduce their share of the world’s GHG emissions. For example, the European Union has implemented a comprehensive climate change initiative aimed at achieving its climate and energy goals by 2050. This ambitious plan includes a target of reducing greenhouse gas emissions by 55% by 2030, compared to 1990 levels, and mandates that 42.5% of the EU’s total energy comes from renewable sources [6]. Additionally, the EU aims for an 11.7% improvement in final energy consumption through enhanced energy efficiency [6]. In the transportation sector, the goal is to increase the share of liquid biofuels to at least 30% by 2030 [7].

While China has several climate change strategies and has actively enacted several climate change policies and action plans, it has still been the largest emitter of CO₂ since 2007, and it currently makes a significant contribution to global emissions. The projected future CO₂ emissions levels are raising serious international concerns [8], while the country has several climate change strategies and has actively enacted several climate change policies and action plans. However, many countries face significant challenges in meeting their climate change targets, largely due to the complexities of integrating certain policies into their economies. The high costs associated with energy efficiency initiatives, carbon pricing, and the development of renewable energy sources can be daunting, particularly in the context of unstable economic and political environments. Consequently, these financial burdens may lead to welfare losses, further complicating efforts to combat climate change effectively [1,9]. Reducing energy consumption subsidies in those countries that pay these decreases overall consumption of households and exacerbates poverty in society [10]. Therefore, researchers suggest that implementing climate change policies, as well as increasing the use of renewable energy and improving energy efficiency technologies, would improve energy security and help to achieve environmental goals [11,12,13].

The above discussion suggested that the transport sector’s energy consumption and GHG emissions are at the core of environmental concerns, and many policies and regulations have been implemented locally or internationally to reduce the level of emissions in this sector. Accordingly, this study first analyses the trend and features of CO₂ emissions and energy consumption in the transport sector in the 10 largest transport CO₂-emitting OECD member countries except the United States (i.e., Japan, Canada, Germany, Mexico, France, the United Kingdom, Korea, Italy, Spain, and Australia) plus New Zealand for comparison. This also includes introducing the largest contributors to total global CO₂ emissions. This information provides important insight for researchers and policymakers for future studies and planning.

This study then provides a systematic literature review on the supply- and demand-side policies that have been implemented for reducing CO₂ emissions or energy consumption worldwide. While many studies highlighted the importance of demand- and supply-side policies in reducing emissions in the economy, the number of studies that have focused on the estimation of the impacts of these policies on transport-related CO₂ emissions is limited. Therefore, this study attempts to fill this gap by investigating the impact of these policies on the reduction of CO₂ emissions in the transport sector at the global level and in New Zealand as a case. Another contribution of this study is that it uses dummy variables to identify the impacts of supply- and demand-side policies on CO₂ emissions in the transport sector.

New Zealand is a good case study, as it produces a high percentage of its total energy supply from different renewable energy sources and has a lower CO₂ emissions energy intensity (CO₂ emissions per total energy supply) compared to the average of Europe, Asia and Oceania, and Australia. The New Zealand transport sector is the major fossil fuel consumer and CO₂ emissions emitter among other economic sectors compared to many OECD countries, especially the OECD Asia and Oceania (The OECD Asia Oceania includes Australia, Israel, Japan, Korea, and New Zealand) [2]. In 2022, it produced about 43% of the total energy supply from renewable energy sources [14]. The CO₂ emissions intensity in 2022 was 35.9 tonnes of CO₂ per terajoule, which, for Europe, Asia and Oceania, and Australia, were 42.1, 60.4, and 66.2 tonnes per terajoule on average [2].

The remainder of this study begins with a review of the literature surrounding methodologies used to analyze supply- and demand-side policies. Section 3 delves into the trends of energy consumption and CO₂ emissions in the leading contributor countries. Section 4 offers a systematic review of the various supply- and demand-side policies. Section 5 outlines the methodology and data used, while the final section presents this study’s conclusions.

2. Literature Review

Over the years, various studies have investigated the evolution and impact of renewable energy and carbon mitigation policies. Zhi et al. [15] analyzed the development of China’s solar photovoltaic (PV) policy and found that government regulations and incentives significantly enhanced investment motivation in the industry. Complementing this, Zhang and Wang [16] conducted a comparative review, showing that high-income countries predominantly rely on demand-side policies, whereas low-income nations favor supply-side strategies.

Fan et al. [17], employing a computable general equilibrium (CGE) model, demonstrated that resource and oil taxes have a minimal impact on carbon emissions, while carbon pricing results in a GDP reduction in China. Similarly, Solaymani [4] used a CGE model to assess Malaysia’s carbon and energy tax policies, finding carbon taxation more effective in reducing emissions. Matsuo [18] proposed a global cap-and-trade framework, aiming to establish a unified upper limit on CO₂ emissions by incorporating both supply- and demand-side participants. Expanding on this, Wang et al. [19] developed an optimization model to examine cap-and-trade mechanisms, revealing that firms’ optimal production levels correlate positively with carbon quota allocations.

Arnette [20] introduced a methodology to compare renewable energy adoption with carbon capture and sequestration, concluding that solar generation is more cost-effective than sequestration strategies. Al-Noaimi et al. [21] employed the Low-Emission Analysis Platform to evaluate scenario-based interventions for Qatar’s energy system, integrating supply and demand perspectives. Mousavi et al. [22] applied decomposition analysis to show that reducing energy subsidies can lower road transport emissions, whereas Tiwari et al. [23] argued that addressing psychological and infrastructure barriers is essential for long-term policy effectiveness. Hafis et al. [24] found that alternative fuels combined with improved engine efficiency were effective in reducing road transport emissions.

Market-based mechanisms have also been widely studied. Ganhammar [25] utilized a two-step procedure to examine the Swedish–Norwegian tradable green certificates market, concluding that price volatility decreases as the market matures. Nie et al. [26], using a microeconomic model, found that subsidies stimulate firm output and improve renewable energy development. Bernini [27], employing a spatial stochastic frontier model, demonstrated that subsidies combined with financial instruments like green bonds enhance investor confidence and market stability. Lin and Zhang [28], however, warned of long-term dependency on subsidies, which could lead to market inefficiencies.

Examining specific incentive policies, Yamamoto [29] assessed feed-in tariffs (FITs) and capital subsidies for household PV adoption, finding that their combination maximizes social welfare. Jeon and Mo [30] simulated supply-side energy storage using a stochastic methodology, revealing that storage lowers the cost of generation. Similarly, Lin and Xie [31] used a regression model to investigate firm-level impacts of FIT subsidies, concluding that these incentives positively influence renewable energy investments.

Lastly, several models have assessed broader energy planning strategies. Huang et al. [32] suggested that gradual carbon reduction should focus on electrification for demand-side policies and expanded local wind and solar capacity for supply-side measures. Hu et al. [33] evaluated green innovation policies using a game model, highlighting the economic complementarities of environmental policies integrated with demand-side interventions. Zhang et al. [34] found that China’s coal capacity cut policy had minimal impact on coal prices and reduced social welfare in the short term. However, Yao et al. [35], using a dynamic CGE model, argued that the policy effectively lowers emissions while promoting GDP growth.

The existing literature highlights the diverse methodologies employed to evaluate the impacts of supply- and demand-side policies, with relatively few studies applying econometric models. Most of these studies focus on individual countries, whereas a broader global framework could provide more comprehensive insights. They employed cross-sectional variables and short-term data to analyze the effects of policies on overall air pollution, excluding contributions from the transportation sector. To address these gaps, the present study investigates the impacts of two policies by applying two econometric models—one renewable energy development policy and another higher transport fuel prices—to assess the impacts of both supply- and demand-side policies on GHG emissions reduction from the transport sector at the global level and in New Zealand as a case study. We used the time series variables for 31 years. During this period, countries implemented different demand- and supply-side policies on the economy to reduce air pollution.

3. Global Transport Emissions and Energy Consumption

The Intergovernmental Panel on Climate Change (IPCC) reveals that, between 1906 and 2005, the average global temperature over land surfaces rose by 0.74 °C. If greenhouse gas emissions continue at the current pace, our planet will confront dire challenges and additional warming. The transportation sector, a major energy consumer, significantly contributes to CO₂ emissions in numerous countries, including the US, Brazil, New Zealand, Australia, and Canada (

Table 2. CO₂ emissions from fuel combustion in major regions and countries (million tonnes).

Region/Country	Total		Transport		Road		Share of Total Emission (%)
							Total		Transport		Road
	2015	2024	2015	2024	2015	2024	2015	2024	2015	2024	2015	2024
World	32,294.2	34,116.8	7737.8	7941.0	5792.0	6022.3	100.0	100.0	24.0	23.3	17.9	17.7
North America	5546.7	5130.9	1925.8	1863.4	1632.8	1549.6	17.2	15.0	34.7	36.3	29.4	30.2
Europe	2641.3	2277.4	788.3	750.7	747.4	704.4	8.2	6.7	29.9	33.0	28.3	30.9
Asia Oceania	1553.7	1357.1	316.9	290.3	279.6	255.3	4.8	4.0	20.4	21.4	18.0	18.8
China &Hong Kong	9084.6	10,644.3	843.9	891.4	698.2	752.8	28.1	31.2	9.3	8.4	7.7	7.1
United States	4997.5	4607.6	1752.0	1699.4	1492.8	1413.5	15.5	13.5	35.1	36.9	29.9	30.7
India	2066.0	2517.0	254.4	323.8	236.5	298.2	6.4	7.4	12.3	12.9	11.5	11.9
Russian Federation	1469.0	1623.2	240.6	267.1	150.4	167.0	4.6	4.8	16.4	16.5	10.2	10.3
Japan	1141.6	973.7	207.8	186.5	187.0	166.3	3.5	2.9	18.2	19.2	16.4	17.1
Germany	729.8	612.0	157.5	141.0	152.4	136.8	2.3	1.8	21.6	23.0	20.9	22.4
South Korea	586.0	549.3	97.1	105.9	92.3	100.0	1.8	1.6	16.6	19.3	15.8	18.2
Iran	552.4	696.4	136.6	142.2	121.4	140.1	1.7	2.0	24.7	20.4	22.0	20.1
Canada	549.2	523.3	173.8	164.0	140.0	136.2	1.7	1.5	31.6	31.3	25.5	26.0
Saudi Arabia	531.5	532.9	142.1	137.8	139.3	135.3	1.7	1.6	26.7	25.9	26.2	25.4
Brazil	450.8	413.9	197.3	212.6	178.5	194.6	1.4	1.2	43.8	51.4	39.6	47.0
Mexico	442.3	379.7	150.5	129.8	145.9	126.2	1.4	1.1	34.0	34.2	33.0	33.2
United Kingdom	389.8	309.4	118.1	107.1	111.7	100.9	1.2	0.9	30.3	34.6	28.7	32.6
Australia	380.9	354.8	94.7	89.2	79.7	75.5	1.2	1.0	0.2	0.3	0.2	0.2
Italy	330.7	310.3	103.0	103.8	97.4	98.9	1.0	0.9	0.3	0.3	0.3	0.3
France	290.5	283.0	122.4	123.3	118.0	116.7	0.9	0.8	0.4	0.4	0.4	0.4
Spain	247.0	217.1	85.5	92.8	78.3	81.6	0.8	0.6	0.3	0.4	0.3	0.4
New Zealand	31.2	28.7	14.4	14.6	13.0	13.6	0.1	0.1	46.2	51.0	41.7	47.3

Source: the author calculated based on the source [2].

).

Countries are actively working to decrease their fossil fuel consumption in the transportation sector while boosting the use of clean and renewable energy sources. For instance, in the United States, energy consumption in the transportation sector declined by 3.2% from 2005 to 2015, dropping from 713.2 million tonnes of oil equivalent (mtoe) to 690.2 mtoe [36]. This trend continued with a further reduction of 1.4% between 2015 and 2021, bringing consumption down to 680.6 mtoe. The decrease in energy consumption within the transport sector is likely a result of the adoption of vehicle efficiency programs and advancements in energy efficiency in this area [37]. The deployment of renewable energy, specifically biomass, within the transportation sector in the United States experienced significant growth from 1074.5 trillion Btu in 2010 to 1351 trillion Btu in 2015, marking a remarkable 25.7% increase. By 2020, this figure reached 1355.5 trillion Btu, reflecting a modest rise of 0.33% since 2015. However, the real breakthrough occurred in 2024, when biomass utilization surged dramatically by 39.9%, soaring to approximately 1896 trillion Btu compared to 2020 [36]. While energy consumption in the transport sector across 27 European countries has seen a decline, dropping from 280 mtoe to 274 mtoe between 2010 and 2021, the adoption of renewable energy sources has experienced remarkable growth [38].

Figure 1 represents the share of transport renewable energy consumption in total energy consumption in major CO₂-emitting OECD countries and New Zealand. It shows that while the share of renewable energy consumption in total energy consumption in the transport sectors in all countries, except Australia and New Zealand, is increasing over time, they are not high, and their use of these kinds of fuels in the transport sector is not significant. For example, France has experienced the largest increase in the share of renewable energy consumption within the transport sector, rising from 0.73% in 2000 to 5.5% in 2010 and 7.1% in 2022. This share for Germany and Canada has increased from 0.40% and 0.26% in 2000 to 6.5% and 3.4%, respectively, in 2020, and settled at 5.8% and 4.2% in 2022. This is due to significant investments in electricity generation from wind and solar sources, along with the importation of green hydrogen from various nations across Europe. For example, in France, the electricity generation from solar increased from 14% in 2010 to 23.4% in 2020 [39]. Figure 1 also demonstrates that almost every EU country is making significant investments in renewable energy production and is notably increasing its renewable energy usage, especially in the transportation sector. Therefore, the dependency of the transport sector on fossil fuel consumption is still high and needs more attention from energy policymakers around the world.

Energy consumption in the transport sector is on the rise, driven by a growing population and an increasing number of vehicles populating our roads. For example, in the transport sector, global oil consumption represented approximately 60% in 2000, 64.9% in 2015, and 63.4% in 2022. Additionally, it constituted around 28.4% of total final energy consumption in 2000, decreased slightly to 28.2% in 2015, and further declined to 26.4% by 2022 [5]. The major contributors to this increase during 2000–2022 were oil products (93.2% on average), natural gas (4% on average), renewable and waste (2% on average), and electricity (1% on average). From 2000 to 2022, the average energy consumption in China’s transport sector represented approximately 13% of the total final energy consumption. This figure varied over the years, hitting a low of 10.7% in 2000 and peaking at 16.1% in 2020 [5]. Moreover, in 2007, Chai et al. [40] demonstrated that the energy consumption intensity in China’s transport sector—measured in kilograms of oil equivalent per dollar of GDP (kgoe/$PPP)—is notably low, approximately 36% of the global average. This may be attributed to the substantial economic value (GDP) of the transportation sector in relation to the fuel it consumes. Wang et al. [41] forecasted a substantial increase in total energy consumption within China’s transport sector, projecting an increase of over 396 million tons from 2004 to 2030.

Table 2 presents the CO₂ emissions from fuel combustion across various regions and countries. From 2015 to 2024, global CO₂ emissions from fuel combustion rose by 7%, with the transport sector experiencing a 3% increase. Throughout this period, the transport sector accounted for approximately 23 to 24% of total global CO₂ emissions. Clearly, North America stands out as the leading region in CO₂ emissions, followed closely by Europe, Asia, and the Pacific. In 2015, 2020, and 2024, China’s contributions to global CO₂ emissions were approximately 28.1%, 32%, and 31.2%, respectively, positioning it ahead of the United States and India on this critical environmental issue. The IEA report reveals that, since 2007, China has surpassed the United States in CO₂ emissions. Specifically, in China, the CO₂ emissions from fuel combustion in the transport sector constituted approximately 9.3% in 2015, 8.9% in 2020, and 8.4% in 2024 of the total carbon emissions. In comparison, the United States’ transport sector accounted for around 35.1% in 2015, 35.4% in 2020, and 30.7% in 2024 of its total CO2 emissions (Table 2). Evidence also shows that the CO₂ emissions from China’s transport sector increased by 6% during 2015–2024 (Table 2). China’s highway transportation sector stands as the foremost contributor to CO₂ emissions [42]. Looking ahead, passenger cars and heavy-duty trucks are anticipated to become significant sources of CO₂ emissions in the country [43].

Table 2 reveals that the ten largest global CO₂-emitting nations in 2015, 2020, and 2022 were China, the United States, and India, followed closely by Russia, Japan, Germany, South Korea, Iran, Canada, and Saudi Arabia. Collectively, these countries accounted for over two-thirds (more than 65%) of the world’s CO₂ emissions. Furthermore, the figures in Table 2 provide information about the 10 largest transport-related CO₂ emitter OECD member countries, except the United States, which include Japan, Germany, South Korea, Canada, Mexico, the United Kingdom, Australia, Italy, France, and Spain. In 2024, they contributed to about 32% of the total OECD transport CO₂ emissions and 15.7% of the world’s total transport CO₂ emissions. Compared to all regions and the countries in Table 2, the transport sector, especially road transport, is the major contributor to the total CO₂ emissions in Brazil and New Zealand.

The level of CO₂ emissions in New Zealand has decreased by 1.3% during 2015–2022, while its share of total CO₂ emissions has not changed, and it is 0.1% of total world CO₂ emissions. Similar to many countries, the transport sector, especially the road transport, is the major contributor to CO₂ emissions in this country. It contributes to 46.2%, 44.9%, and 51% of total CO₂ emissions in 2015, 2020, and 2022, whereas road transport contributes to 41.7%, 41.4%, and 47.2% of total CO₂ emissions. The decline in total and transport CO₂ emissions in this country is due to the implementation of environmental policies to encourage people to use electric cars.

Numerous studies have explored energy consumption and GHG emissions within the transport sector across various countries and regions. Kharbach and Chfadi [44] found that Morocco could experience a decrease in transport sector emissions as a result of economic growth, likely driven by the liberalization of petroleum products and an increased reliance on public transportation. Additionally, Song et al. [45] highlighted that, in China, substituting diesel heavy-duty vehicles with liquefied natural gas significantly curtails GHG emissions. Kelly et al. [46] highlighted the profound impact of public transportation on energy consumption and greenhouse gas emissions within society. In alignment with this, Hao et al. [47] and Wang et al. [48] assert that, to effectively curb energy consumption and CO₂ emissions, the Chinese government must introduce stringent vehicle fuel economy standards alongside energy and CO₂ taxes on conventional transportation.

Figure 2 illustrates the energy consumption patterns of the largest CO₂-emitting transport sectors within OECD countries, except the United States. It indicates that the use of fossil fuels in transportation has increased in some countries while decreasing in others. Notably, the level of consumption across all countries declined in 2020 due to the COVID-19 pandemic. Japan’s transportation saw a significant decline of 28% in fossil fuel consumption from 2000 to 2022, while Korea recorded the largest increase at 36.5%. The fossil fuel consumption of Japan’s transport decreased substantially from 3731 petajoules (PJ) in 2000 to 2972 PJ in 2015 and 2673 PJ in 2020 and rose again to 2692 PJ in 2022. Canada’s transport fossil fuel consumption decreased by 1%, from 2177.5 PJ in 2000 to 2151.2 PJ in 2020, before rising by 10.5% by 2022 compared to the 2020 value. This trend also occurred in Australia, Spain, New Zealand, Mexico, and Korea. However, Australian transportation saw higher fossil fuel consumption in 2020 compared to 2000, although it declined by 7% relative to the previous year (2019).

The U.S. transportation sector stands as the primary source of the nation’s total CO₂ emissions, highlighting its significant impact on environmental concerns. This is also evidenced in other countries, like Tunisia. Mraïhi and Harizi [49] have identified that the primary factors contributing to emissions growth in Tunisia are energy and transport intensities. They suggested that to mitigate transport intensity, a crucial step is to transition road freight to rail transport. In India, diesel vehicles are the main contributor to CO₂ emissions [50]. However, helpful instruments to improve energy efficiency include using clean energy and clean vehicles [49].

The share of electricity in the transport sector in major OECD countries is increasing, as represented in Figure 3. For example, in Italy, the share of electricity in transport total energy consumption in 2000 was 1.84%, which increased to 2.57% in 2015, and then to 3% in 2020 and 2.1% in 2022. While Italy has the highest motorization rate among European countries, this is primarily due to the development and expansion of electric and hybrid vehicles, which accounted for approximately 4.6% of all registered cars in Italy in 2021 [51]. This share for New Zealand is not high, and it is about 0.15% in 2000, and 0.18% and 0.34% in 2020 and 2022, respectively, while it has increased over time. These increases happened because of implementing some policies that motivate people to use and buy electric and hybrid vehicles, such as the New Zealand Clean Vehicle Discount Scheme [52], the Energy Efficiency and Conservation Act 2000 [53], Equipment Energy Efficiency, Vehicle Emissions, and Energy Economy [54].

Between 2000 and 2022, many countries increased their use of electricity in the transportation sector, except Germany, Japan, Spain, and the United Kingdom. Electricity consumption in the transportation sector in Germany has decreased by 17.4%, while, in Japan, it has decreased by 11%. Australia experienced the greatest increase (189%), followed by New Zealand (166%).

With the transport sector accounting for a significant and escalating portion of energy consumption and CO₂ emissions, it is imperative that we implement changes to address this pressing issue. One viable solution lies in the development of robust and effective policies specifically designed to tackle these challenges. Strategic policy interventions and regulations can play a crucial role in reducing greenhouse gas emissions generated by the transport sector [55,56]. For example, technological advancements in vehicles, the adoption of more fuel-efficient models, and increased investments in electric and hybrid alternatives collectively contribute to a reduction in total greenhouse gas emissions from the transportation sector [57,58,59,60]. This paper reviews the policies used worldwide for reducing transport carbon emissions and analyzes them from both supply-side and demand-side perspectives.

4. Demand- and Supply-Side Policies

Investigating policies on energy and carbon emissions from the literature reveals that the government can intervene on both sides of the economy to implement specific environmental policies to achieve its environmental targets [15,42]. Improving energy efficiency and intensifying the use of biofuels, particularly in road transport, are traditionally two key areas for the sustainability of the transport sector [61]. Accordingly, this study focuses on both sides of the economy and investigates demand- and supply-side policies in countries that implement climate change policies for reducing energy and carbon emissions.

Demand-side and supply-side policies play crucial roles in reducing CO₂ emissions and promoting renewable energy generation. This review synthesizes the key findings from the reviewed literature, covering various policy instruments, their implementation, and their effectiveness.

4.1. Demand-Side Policies

Carbon taxes are levied on carbon dioxide emissions to incentivize emission reductions. European countries have introduced carbon taxes extensively [62]. Fan et al. [17] demonstrate that combining carbon taxes with green development policies enhances emission reductions without significant economic drawbacks. Arnette [20] suggests that carbon taxes paired with tax incentives for renewable energy can substantially reduce CO₂ emissions. Carbon taxes can have regressive impacts and may vary in effectiveness across regions and sectors [63]. Research by Dong et al. [64] indicates that carbon taxes can impede economic development, though Solaymani [4] provides nuanced insights into these effects. Overall, carbon taxes are effective in reducing emissions when combined with complementary policies, but careful design is necessary to mitigate economic and social impacts.

The Emissions Trading Scheme (ETS) relies on a cap-and-trade system where businesses buy and sell emissions allowances. Heinrichs et al. [65] showed that including road transport in an ETS decreases overall CO₂ emissions. Matsuo [18] proposed a global cap-and-trade framework to set a worldwide upper limit on CO₂ emissions, integrating both supply- and demand-side actors. The challenges of having this policy are that the policy impacts firms’ production and operation, and the transport sector’s response may be slower and less substantial [19]. An ETS can significantly reduce emissions, particularly when integrated globally, but sector-specific responses and operational impacts must be considered.

Phasing out fossil fuel subsidies can significantly reduce emissions by encouraging the adoption of alternative fuels [22]. Al-Noaimi et al. [21] suggested that subsidy reductions could achieve significant GHG emissions reductions. Subsidy reductions in Qatar’s building sector achieved a 27% reduction in GHG emissions [21]. Mousavi et al. [22] demonstrated emission reductions in Iran by encouraging a switch from gasoline to natural gas in the transport sector. Tiwari et al. [23] identify significant psychological barriers to electric vehicle adoption, including concerns over charging infrastructure reliability, range limitations, and vehicle power. Reducing energy subsidies is effective in driving emissions reductions, but addressing psychological and infrastructure barriers is essential for long-term success.

Fuel-switching policies encouraging a switch from fossil fuels to alternative fuels like biofuels, hydrogen, and electricity are essential for reducing transport-related emissions [66]. Recent studies confirm the effectiveness of fuel switching in reducing emissions, depending on infrastructure availability and policy incentives [24]. Timilsina et al. [67] emphasize the importance of technological advancements in improving fuel efficiency. Fuel switching is a highly effective demand-side strategy, contingent on supportive infrastructure and technological advancements.

Smart Energy Management means integration of AI-driven strategies to optimize demand response and reduce emissions while maintaining grid reliability [68]. Multi-scale optimization frameworks can achieve higher emissions savings through coordinated demand-side management efforts [69]. Smart energy management, particularly through AI integration, offers promising emissions reductions and grid stability benefits.

Tradable Green Certificates (TGCs) encourage electricity generation from renewable sources through a complementary market for green certificates [70]. Applied in countries like the UK, Belgium, and Sweden, TGCs promote market-driven renewable energy growth. TGCs significantly reduce peak electricity demand and annual electricity savings [71]. Market instability and price fluctuations pose challenges, deterring long-term investment in renewables [25]. Shrimali and Tirumalachetty [72] highlight the fact that uncertainty in the green certificate market can deter long-term investment in renewables. TGCs can effectively drive renewable energy generation in the short term, but market stability and regulatory certainty are critical for long-term success.

4.2. Supply-Side Policies

Government Support for Renewable Energy, such as government subsidies for renewable energy generation, supports the development and expansion of clean energy sources [26]. Bernini [27] demonstrates that combining subsidies with financial instruments like green bonds enhances investor confidence and market stability. Subsidies increase renewable energy firm outputs and improve shareholder value [26]. Lin and Zhang [28] warn against long-term dependency on subsidies, as this can lead to inefficiencies and distort market competition. Long-term dependency on subsidies can lead to inefficiencies and market distortions [29,73]. Energy storage technologies enhance grid reliability and improve energy affordability [30]. Government subsidies are vital for renewable energy growth, but policy design must ensure long-term economic viability and market stability.

Feed-in Tariffs (FiTs) guarantee the purchase of electricity at above-market prices, encouraging renewable energy investment [74]. Lin and Xie [31] reinforce the idea that FiTs have successfully increased investments in solar PV and wind energy, especially in China. Nicolini and Tavoni [75] found that FiTs perform more effectively than tradable green certificates, leading to an immediate rise in renewable generation. Wang et al. [76] emphasize the positive impact on solar PV firms and sustainable development in developing countries. Policy uncertainty and declining profitability pose challenges to long-term investment stability [77,78]. Wong et al. [79] highlight the need for careful energy policy design to address cost-effectiveness, network losses, and other issues. FiTs are highly effective in driving renewable energy investments, but addressing policy uncertainty and profitability challenges is essential for sustained success.

Coal Capacity Cut focuses on reducing coal capacity, which optimizes resource allocation and reduces CO₂ emissions [35]. The policy may negatively impact social and economic factors [34]. Cutting coal capacity is effective for CO₂ emission reduction, but its social and economic impacts must be carefully managed.

4.3. Integrative Approaches

Combining both approaches yields more enduring emissions reductions and strengthens market resilience [33]. However, the challenge is that supply-side policies depend on economic stability and consistent government support. A combined approach of demand-side and supply-side policies offers the most resilient and sustainable solution for long-term CO₂ reduction.

Urban Energy Planning, such as electrification initiatives and innovative energy transition strategies in megacities, can significantly reduce emissions [32]. Infrastructure investment and urban planning are critical for achieving substantial emission reductions. Urban energy planning is crucial for significant emission reductions, highlighting the importance of targeted infrastructure investments.

In conclusion, demand-side and supply-side policies are both crucial for reducing CO₂ emissions and promoting renewable energy. Each policy instrument has its own strengths, challenges, and contextual dependencies. Integrating these approaches and leveraging technological advancements, such as AI-driven strategies, can enhance the effectiveness of energy management and carbon reduction efforts. Continued research and policy innovation are essential for achieving a sustainable and equitable energy future.

5. Methodology

We conducted a thorough meta-analysis of the policies used to reduce energy and CO₂ emissions from a literature review of relevant papers within the Scopus and Science direct academic databases. The literature retrieval was mainly based on those journals that have significant publications regarding carbon emissions, such as Energy Policy, Journal of Cleaner Production, Energy, Applied Energy, and Renewable Energy, Renewable and Sustainable Energy Reviews. To support the literature review, we implemented one supply-side and one demand-side policy on the world transport CO₂ emissions and New Zealand’s transport sector using Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) methods. We employ these methods to analyze the effects of one supply-side and one demand-side policy on CO₂ emissions reduction, both globally and specifically in the New Zealand case. Figure 4 shows a structure for this study’s methodology.

The FMOLS and DOLS methods are widely used in the literature to investigate the impacts of different issues. For example, Khan et al. [80], using these methods, estimated the impacts of CO₂ emissions and economic growth on renewable energy production in ASEAN countries. Streimikiene and Kasperowicz [81] also used these methods to investigate the long-run relationship between economic growth and energy demand in EU-18. Fatima et al. [82] used the DOLS, FMOLS, and MMQR methods to investigate the impacts of environmental policy stringency on technological innovation and CO₂ emissions in G7 countries. Wei and Ullah [83] used the FMOLS and DOLS methods. These methods are used to investigate the long-run links between the following variables.

GHG_t = f (pGDP_t, FFC_t, URBP_t, FP_t)

(1)

In Equation (1), GHG_t is the greenhouse gas emissions from the world and New Zealand transport sectors over time t (from 1990 to 2020). pGDP_t is the per capita GDP based on 2015 US constant prices over time. FFC_t is the fossil fuel energy consumption (petajoules), and URBP_t is the number of populations living in urban areas over time. FP_t is the average price of gasoline and diesel in the United States (US dollar per gallon and the NZ$ per litre) over time. We can rewrite Equation (1) in natural logarithmic form as follows:

LGHG_t = α₀ + α₁LpGDP_t + α₂LFFC_t + α₃LURBP_t + α₄LFP_t + u_t

(2)

In Equation (2), u_t is the error term. We use two dummy variables to investigate the impact of demand- and supply-side policies. We use renewable energy development (DUMR) to investigate the impact of government support on renewable energy development. Governments have used various policies, and, since the late 1990s, attempted to increase motivation for investment and development of renewable energies. Since these kinds of policies were used and implemented in the economy by the majority of countries during the 2010s, we applied this dummy variable in the study model during 2015–2020. To evaluate the impact of increases in fuel prices in the transport sector, we used another dummy variable (DUMP) that increased the price of transport fuel consumption during 2015–2020, which will be multiplied by the fuel prices. This will implement a 5% increase in fuel prices during 2015–2020, effectively, and show how it affects the transport-related CO₂ emissions in this study. Therefore, the final econometric model of this study can be presented as in Equation (3):

LGHG_t = α₀ + α₁LpGDP_t + α₂LFFC_t + α₃LURBP_t + α₄LFP_t + α₅DUMR_t + α₆(FP.DUMP)_t + u_t

(3)

In this study, we estimate four models using both FMOLS and DOLS estimators. The first model is the base model without the dummy variables, which is Equation (1). The second model is the base model with the first dummy variable, i.e., renewable energy development (DUMR). The third model is the base model with the second dummy variable, i.e., fuel oil price change (FP*DUMP). Finally, the fourth model is the base model with both dummy variables, which shows the impact of both policies on the transport CO₂ emissions.

The data for the above models were collected from various sources and covered the period from 1990 to 2020. Energy and GHG emissions data were collected from GHG highlights and world energy balance highlights of the International Energy Agency (IEA). The per capita gross domestic product (pGDP) was gathered from the World Bank database (WDI). Gasoline and diesel fuel prices were collected from the US Energy Information Administration (EIA).

5.1. DOLS and FMOLS Estimators

In this study, to estimate Equations (2) and (3), we use the DOLS and FMOLS estimators. The DOLS procedure employs independent indicators, incorporating leads and lags of their initial difference expressions, to effectively manage endogeneity. It also calculates standard errors using a covariance matrix of errors that is robust to autocorrelation. The DOLS estimators provide a reliable gauge of statistical significance. Evaluating the endogenous indicator against exogenous indicators at various levels, leads, and lags proves to be an effective approach for addressing diverse orders of integration. This method facilitates the integration of different elements within the cointegrated framework [84]. Conversely, the primary strength of DOLS predictions resides in their capacity to accommodate varying orders of integration among diverse factors within a cointegrated framework [85,86]. This approximation mitigates issues such as shorter sample bias, endogeneity, and autocorrelation by harmonizing the timing of each explanatory variable’s measurement [87]. The DOLS estimator function based on Equation (3) is presented in Equation (4).

$$\begin{gathered} ∆LGH{G}_t={\gamma }_0+{\sum }_{t=1}^t{\gamma }_1∆LpGD{P}_{t-1}+{\sum }_{t=1}^t{\gamma }_2∆LFF{C}_{t-1}+{\sum }_{t=1}^t{\gamma }_3∆L{URBP}_{t-1}+ \\ {\sum }_{t=1}^t{\gamma }_4∆L{FP}_{t-1}+{\sum }_{t=1}^t{\gamma }_5∆{DUMR}_{t-1}+{\sum }_{t=1}^t{\gamma }_6∆{(FP\times DUMP)}_{t-1}+{\delta }_1∆LpGD{P}_{t-1}+ \\ {\delta }_2∆L{FFC}_{t-1}+{\delta }_3∆L{URBP}_{t-1}+{\delta }_4∆LF{P}_{t-1}+{\delta }_5∆{DUMR}_{t-1}+{\delta }_6∆({FP\times DUMP)}_{t-1}+{\epsilon }_t \end{gathered}$$

(4)

To validate the results of the DOLS analysis, this study used the Fully Modified OLS (FMOLS) method. The FMOLS approach, introduced by Phillips and Hansen [88], enhances the ordinary least squares method by effectively tackling cointegration, along with addressing issues of autocorrelation and endogeneity in the explanatory variables. The overall information about all variables used in this study is reported in

Table 3. Descriptive statistics.

	World					New Zealand
	GHG	FFC	pGDP	URBP	FP	GHG	FFC	pGDP	URBP	FP
Mean	22.58	11.38	9.04	21.87	0.64	3.36	10.60	5.15	15.07	5.14
Median	22.60	11.40	9.04	21.87	0.60	3.43	10.64	5.23	15.08	5.12
Maximum	22.86	11.66	9.30	22.18	1.33	3.54	10.85	5.39	15.28	5.43
Minimum	22.28	11.08	8.82	21.55	0.04	3.04	10.34	4.78	14.85	4.82
Std. Dev.	0.19	0.19	0.16	0.20	0.47	0.15	0.16	0.18	0.11	0.19
Skewness	−0.11	−0.10	0.05	−0.01	0.11	−0.78	−0.27	−0.75	0.03	0.03
Kurtosis	1.72	1.74	1.65	1.75	1.41	2.24	1.77	2.51	2.10	1.67
Jarque-Bera	2.02	1.96	2.21	1.88	3.09	3.63	2.18	3.03	0.97	2.13
Probability	0.36	0.38	0.33	0.39	0.21	0.16	0.34	0.22	0.61	0.35

Note: all variables are in their natural logarithmic values.

.

5.2. Testing for the Stationarity

Before providing estimates for the econometric models, we need to investigate the existence of a unit root in the models’ variables. To do this, different unit root tests were applied. The results of these tests are presented in

Table 4. Test results of unit roots.

		ADF	DF-GLS	PP	KPSS
World	GHG	−1.765	−0.526	−1.463	0.72 c
	ΔGHG	−4.181 b	−3.695 b	−4.181 b	0.270
	pGDP	−0.612	−0.869	−0.612	0.719 c
	ΔpGDP	−3.378 b	−2.674 b	−2.739 b	0.147
	URBP	−0.964	−0.669	−1.440	0.733 c
	ΔURBP	−1.666 d	−1.861	−2.641 c	0.652 c
	FFC	−1.730	0.333	−0.458	0.699 c
	ΔFFC	−3.604 c	−3.620 b	−3.544 c	0.07
	FP	−1.086	−0.913	−1.085	0.596 c
	ΔPF	−4.732 b	−4.096 b	−4.688 b	0.175
New Zealand	GHG	−2.023	−0.733	−2.235	0.644 c
	ΔGHG	−7.527 b	−7.224 b	−7.311 b	0.204
	GDP	0.207	−0.694	0.207	0.726 b
	ΔGDP	−3.874 b	−2.909 b	−3.790 b	0.114
	URBP	−0.941	0.346	−3.406 c	0.631
	ΔURBP	−5.275	−4.501	−5.275	0.285 b
	FFC	−2.965 c	−1.891 d	−3.351 c	0.663 c
	ΔFFC	−2.409	−1.523	−1.668	0.516
	FP	−1.266	−1.288	−1.263	0.516c
	ΔPF	−5.177 b	−4.826 b	−5.168 b	0.150

^b,^c,^d are the levels of rejection of the null hypothesis at 1, 5, and 10%, respectively.

. These tests were performed for the level and the first difference of the models’ variables. The results show that all variables are stationary at their levels. Therefore, the null hypothesis of the existence of a unit root for the ADF (Augmented Dickey–Fuller), DF-GLS (Dickey–Fuller Generalized Least Squares), and PP (Phillips–Perron) tests was rejected, while, for the KPSS (Kwiatkowski–Phillips–Schmidt–Shin) test, for which the null hypothesis is stationary, it is accepted. We can conclude that all variables are stationary and integrated of order I in their first difference.

6. Results and Discussion

The results of FMOLS and DOLS models for the global economy are reported in

Table 5. Results of the estimated models for world GHG emissions.

	Base Model		Model 1		Model 2		Model 3
	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS
	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]
pGDP	0.298 [0.203]	0.073 [0.144]	0.793 b [0.221]	0.664 c [0.279]	0.275 [0.109]	0.261 c [0.105]	0.349 b [0.069]	0.318 b [0.070]
FFC	0.387 b [0.082]	0.328 [0.266]	0.250 b [0.075]	0.275 b [0.095]	0.324 b [0.052]	0.328 b [0.052]	0.271 b [0.031]	0.286 b [0.036]
URBP	0.361 b [0.116]	14.075 d [8.173]	0.209 c [0.102]	0.272 c [0.124]	0.473 b 0.066]	0.481 b [0.064]	0.481 b [0.041]	0.491 b [0.042]
FP	−0.007 [0.009]	0.071 [0.094]	−0.044 b [0.015]	−0.036 d [0.021]	-	-	-	-
DUMR	-	-	−0.030 b [0.011]	−0.027 d [0.015]	-	-	−0.022 b [0.002]	−0.022 b [0.003]
FP × DUMP	-	-	-	-	−0.021 b [0.003]	−0.021 b [0.004]	−0.011 c [0.002]	−0.010 b [0.003]
C	7.592 b [1.220]	−63.072 d [34.934]	8.031 b [0.966]	7.517 b [1.067]	6.065 b [0.754]	5.975 b [0.713]	5.825 b [0.478]	5.722 b [0.470]

^b,^c,^d are the levels of significance of the figures at 1, 5, and 10%, respectively.

. The results of the base model show that GDP per capita positively affects GHG emissions in the transport sector significantly. The coefficient of fossil fuel consumption in the transport sector is positive and shows that, when fossil fuel use increases by 1%, global GHG emissions increase by 0.39%. By increasing the world’s urban population, the use of the transport sector increases, resulting in more use of fossil fuels and transport GHG emissions. According to the coefficient of this variable, with an increase of 1% in urbanization, the total amount of GHG that enters the atmosphere increases by 0.36% in the long run. The coefficient of urbanization is lower than the fossil fuel consumption, which is most likely due to more people using public transport and electric vehicles. Since there is a direct relationship between fossil fuel consumption and GHG emissions, we can assume that the amount of GHG emissions is as much as the demand for fossil fuels.

According to microeconomic theory, the price of a commodity, like a fossil fuel, is a key determinant of its demand function, illustrating an inverse relationship where higher prices typically lead to lower demand. For the case of this study, a negative sign exists for the coefficient of the average fuel prices in the transport sector, while it is not significant. The results of model 1 show that when a supply-side policy, i.e., the renewable energy development, applies to the model, the impact of this policy on transport GHG emissions is negative, implying that, with an increase of 1% in the government support of renewable energy development, GHG emissions in the transport sector decrease initially by 0.03% due to a reduction in the use of fossil fuels in the transport sector. When a demand-side policy, i.e., a 5% increase in transport fuel prices, is implemented in the model, this policy reduces the transport GHG emissions by 0.02%. This finding shows that the price elasticity of demand for transport fuel is inelastic.

It is worth noting that the magnitude of the impact of government support on renewable energy development is greater than the increases in transport fuel prices. Finally, when both policies, a 5% increase in transport fuel and renewable energy development, are implemented in the model, the results show that both policies are effective in reducing GHG emissions in the transport sector. The magnitude of the impacts of both policies is greater than that of the single policy. The magnitude of the impacts of both policies is 0.033 (0.022 for the supply-side policy and 0.011 for the demand-side policy), while the magnitude of the impact of the supply-side policy is 0.030 and the demand-side policy is 0.021. In general, the results show that the supply-side policy complements the demand-side policy, which supports the previous results of Yi and Feiock [89].

Table 6. Results of the estimated models for New Zealand GHG emissions.

	Base Model		Model 1		Model 2		Model 3
	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS	FMOLS	DOLS
	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]
pGDP	1.075 d [0.564]	0.679 [0.467]	0.931 d [0.546]	0.629 [0.496]	1.020 c [0.408]	0.593 d [0.324]	0.683 [0.405]	0.418 [0.293]
FFC	0.574 d [0.284]	0.692 b [0.248]	0.505 d [0.252]	0.584 c [0.279]	0.492 c [0.217]	0.582 b [0.200]	0.347 c [0.171]	0.391 c [0.191]
URBP	−2.231 c [1.019]	−0.756 d [0.435]	−0.680 [0.606]	−0.369 [0.569]	−0.766 [0.465]	−0.257 [0.416]	0.270 [0.622]	0.581 [0.493]
FP	−0.113 [0.093]	−0.067 [0.107]	−0.157 d [0.084]	−0.100 [0.117]	-	-	-	-
DUMR	-	-	−0.036 [0.046]	−0.066 [0.057]	-	-	−0.024 b [0.006]	−0.021 b [0.007]
FP × DUMP	-	-	-	-	−0.016 b [0.006]	−0.015 c [0.007]	−0.087 c [0.041]	−0.104 c [0.041]
C	22.057 c [10.207]	4.454 [3.687]	2.060 [5.161]	−0.167 [5.582]	1.726 [4.293]	−1.976 [4.366]	−9.719 [6.075]	−11.864 c [5.482]

^b,^c,^d are the levels of significance of the figures at 1, 5, and 10%, respectively.

reports the results for the New Zealand case. The base model results show that economic growth has a positive relationship with GHG emissions in the transport sector. It implies that, with a 1% increase in economic growth, the GHG emissions increase by 1.08% in the long run. These results align with the study by Kwilinski et al. [90], which demonstrated that GDP positively influences CO₂ emissions from the transport sector. Increasing fossil fuel consumption in the transport sector increases GHG emissions such that, when the demand for fossil fuel increases by 1%, GHG emissions increase by 0.57%. The coefficient of urban population is negative and shows a negative link between urban population and GHG emissions. This is because there has not been a significant change in the urban population of the country during the study period. For example, with an average of a 1.4% population growth rate during 1990–2020, in 1990, about 85% of the total NZ population lived in urban areas, which increased to 86% in the year 2000 and 87% in 2020. The coefficient of transport fuel prices shows a negative relationship between GHG emissions (or demand for fossil fuels) and fuel prices, but it is not statistically significant.

The results of model 1 show that the coefficient of RED (renewable energy development) represents a negative impact of supply-side policy on the GHG emissions in this country, although it is not statistically significant. However, the negative and statistically significant coefficient of transport fuel prices (DUMP) shows that the demand side policy is effective in reducing GHG emissions in the transport sector, but with an initial magnitude (Model 2). A key factor behind this outcome is the influence of increasing fuel prices on consumer behavior, prompting individuals to opt for more fuel-efficient vehicles and invest in electric and hybrid models. These advancements not only contribute to lowering greenhouse gas emissions from road transport but also diminish dependence on fluctuating fossil fuel prices [59,60].

Finally, the results of model 3, which includes both policies, show that both policies together are effective in reducing GHG emissions in the transport sector. It shows that when a renewable energy development policy and a 5% increase in transport fuel happen, GHG emissions in the transport sector decrease by 0.02% and 0.09%, respectively. The total magnitude of the decrease in GHG emissions is about 0.11, which is greater than each policy.

To check the robustness of the results of both cases, we performed two estimations based on ordinary least squares (OLS) and canonical cointegrating regression (CCR) (refer to

Table A1. The results of OLS and CCR estimations.

	Base Model (World)		Model 3 (World)		Base Model (NZ)		Model 3 (NZ)
	OLS	CCR	OLS	CCR	OLS	CCR	OLS	CCR
	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]	Coeff. [Std. Err.]
C	7.311 b [0.929]	8.051 b [1.198]	5.722 b [0.584]	4.756 [0.846]	4.454 [3.008]	4.454 [3.687]	−11.864 b [4.956]	−11.864 c [5.482]
PGDP	0.267 d [0.157]	0.208 [0.180]	0.318 b [0.087]	0.429 b [0.091]	0.679 d [0.381]	0.679 [0.467]	0.418 [0.265]	0.418 [0.293]
ENINT	0.374 b [0.069]	0.509 b [0.167]	0.286 b [0.045]	0.114 [0.128]	0.692 b [0.202]	0.692 b [0.248]	0.391 c [0.173]	0.391 c [0.191]
URBP	0.393 b [0.093]	0.314 c [0.121]	0.491 b [0.052]	0.578 b [0.078]	−0.756 c [0.355]	−0.756 d [0.435]	0.581 [0.446]	0.581 [0.493]
FP	−0.003 [0.009]	−0.005 [0.009]	-	-	−0.067 [0.087]	−0.067 [0.107]	-	-
DUMR	-	-	−0.022 b [0.003]	−0.027 b [0.004]	-	-	−0.021 b [0.006]	−0.021 b [0.007]
FP × DUMP	-	-	−0.010 c [0.004]	−0.012 b [0.003]	-	-	−0.104 b [0.037]	−0.104 c [0.041]

^b,^c,^d are the levels of significance of the figures at 1, 5, and 10%, respectively.

in Appendix A). The results of these estimators support the results reported in Table 5 and Table 6.

7. Conclusions and Suggestions

Energy is the primary driver of all human activities and has significantly transformed our daily lives. Initially, the primary sources of energy for movement were wood and coal, followed by other fuels. Over time, increased economic growth and development have dramatically raised the demand for fossil fuels. Since the world oil crisis in the early 1970s, the consideration of energy policy-related topics has leaned towards the use of alternative energies and energy security. This has increased concerns about the environmental challenges from the use of fossil fuels. The production and consumption of renewable energy resources and environmental policies have reduced the growth rate of fossil fuel consumption in the economy more than when these policies and energy sources were not generated and implemented. While the use of fossil fuels has increased dramatically since the 1970s, many countries have had significant policies to reduce the use of fossil fuels.

These policies are present and implemented on both the demand and supply sides of the energy market. The number of demand-side policies exceeds that of supply-side policies, making them easier to use and effective in the short term. This is because, when governments reduce fuel subsidies, fuel consumption in the transport sector reduces significantly in the first 6–12 months of policy application, then the consumption of fuel increases slowly [91]. Since these kinds of policies increase transport fuel prices, they may have an initial impact on the reduction of CO₂ emissions from the transport sector because the price elasticity of demand for transport fuels is low due to a lack of appropriate substitute fuels. The supply-side policies affect the production of energy from renewable energy sources. These policy instruments need specific infrastructures and have made a significant change in the generation of clean energy sources globally. However, there are some uncertainties in the effectiveness of these policies, and they depend on the political and economic conditions of the country in question.

The results of econometric models showed that the supply-side policy and demand-side policy can help in reducing GHG emissions in the transport sector separately, while their effects are greater when both are implemented together. It is worth noting that the magnitude of the impact of government support on renewable energy development is greater than the increases in transport fuel prices. In the New Zealand case, the supply-side policy, renewable energy development, has a negative impact on GHG emissions, but this impact has lower magnitude than the impact of the supply-side policy. When both policies are implemented together in the economy, they can significantly help the environmental sustainability of the economy. This means that the magnitude of the impacts of the joint implementation of both policies is greater than that of each policy individually.

Based on the reviewed literature and the econometric results, the implementation of both policies is effective in achieving sustainable development. However, it is necessary to understand the complexity of various tool mixes. Analysts should shift their focus from individual tools to the effectiveness of combinations of policy instruments that simultaneously target both the supply and demand sides of the market.