Dynamic Autoregressive Distributed Lag Research Articles

Energy Transition, fossil Fuels, and green Innovations: Paving the way to achieving sustainable development Goals in the United States

The United States, as the world's second-largest polluter, emitted a staggering 4.7 billion metric tonnes of carbon dioxide in 2020. During the Leaders’ Climate Summit, the US president articulated precise targets for combating climate change, notably a 50 to 52 percent reduction in net CO2 emissions from 2005 levels by 2030. Consequently, it becomes imperative to identify the pivotal factors that are driving this intensified dedication to achieving the Sustainable Development Goals (SDGs). Hence, it is vibrant to determine the basic causes propelling the intensified interest in meeting SDGs 7 and 13. From this perspective, this study explores the dynamic link between energy transition, fossil fuel energy, green innovation, and economic complexity index on CO2 emissions and ecological footprint in the United States from 1980 to 2020. The dynamic Autoregressive-Distributed Lag (ARDL) simulations and Frequency Domain Causality (FDC) techniques are applied for long-medium and short-run causal association among the variables. Our empirical evidence shows that energy transition, fossil fuel energy, and economic complexity increase CO2 emissions and ecological footprint. On the other hand, green innovation reduces CO2 emissions and ecological impact only in the long term. The pragmatic outcomes suggest that authorities should inhibit Fossil Fuel Energy, concentrate on energy transitions, and include more renewable sources in the US energy mix. The findings would assist the United States in meeting SDGs 7 and 13.

Reshaping energy horizon of Iran: investigating economic sanctions, export diversification, and environmental resilience.

Employing robust methodologies, including principal component analysis, autoregressive moving average, Fourier bootstrap dynamic autoregressive distributed lag, error correction model, and the Breitung-Candelon spectral Granger causality test, this study scrutinizes the impact of export diversification (EXD) on Iran's ecological footprint (EF) from 1997 to 2020, considering economic sanctions (ESI), trade openness (TOP), energy consumption per capita (ECpc), globalization (KOF), and real GDP per capita (RGDPpc). Findings consistently affirm a positive environmental impact of EXD, revealing a nuanced temporal pattern. Notably, the short-term impact (- 0.645) is more pronounced than its long-term counterpart (- .020). Increased industrial activities due to globalization (10% rise) lead to 4.26% and 1.64% EF degradation in the long and short term. Conversely, due to Iran's heavy reliance on fossil fuels, a 10% rise in ECpc correlates with 1.63% and 3.81% long- and short-term environmental quality reduction. ESI demonstrates a dual impact, improving short-term environmental quality but contributing to long-term degradation. Frequency-domain causality analysis highlights EXD and KOF as short- and long-term causes of EF, ESI, and TOP as medium- to long-term causes and RGDPpc as a long-term cause. These findings emphasize the need for sustainable policies, stringent environmental standards, and a balanced approach to fostering economic growth while preserving the environment.

Predicting the short and long term effects of food price inflation, armed conflicts, and climate variability on global acute malnutrition in Somalia

BackgroundMalnutrition poses a substantial challenge in Somalia, impacting approximately 1.8 million children. This critical issue is exacerbated by a multifaceted interplay of factors. Consequently, this study seeks to examine the long-term and short-term effects of armed conflicts, food price inflation, and climate variability on global acute malnutrition in Somalia.MethodsThe study utilized secondary data spanning from January 2015 to December 2022, sourced from relevant databases. Two distinct analytical approaches were employed to comprehensively investigate the dynamics of global acute malnutrition in Somalia. Firstly, dynamic autoregressive distributed lag (ARDL) simulations were applied, allowing for a nuanced understanding of the short and long-term effects of armed conflicts, food price inflation, and climate variability on malnutrition. Additionally, the study employed kernel-based regularized least squares, a sophisticated statistical technique, to further enhance the robustness of the findings. The analysis was conducted using STATA version 17.ResultsIn the short run, armed conflicts and food price inflation exhibit positive associations with global acute malnutrition, particularly in conflict-prone areas and during inflationary periods. Moreover, climatic variables, specifically temperature and rainfall, demonstrate positive associations. It is important to note that temperature lacks a statistically significant relationship with global acute malnutrition in the short run. In the long run, armed conflicts and food price inflation maintain persistent impacts on global acute malnutrition, as confirmed by the dynamic ARDL simulations model. Furthermore, both temperature and rainfall continue to show positive associations with global acute malnutrition, but it is worth noting that temperature still exhibits a non-significant relationship. The results from kernel-based regularized least squares were consistent, further enhancing the robustness of the findings.ConclusionsIncreased armed conflicts, food price inflation, temperature, and rainfall were associated with increased global acute malnutrition. Strategies such as stabilizing conflict-prone regions, diplomatic interventions, and peace-building initiatives are crucial, along with measures to control food price inflation. Implementing climate adaptation strategies is vital to counter temperature changes and fluctuating rainfall patterns, emphasizing the need for resilience-building. Policymakers and humanitarian organizations can leverage these insights to design targeted interventions, focusing on conflict resolution, food security, and climate resilience to enhance Somalia's overall nutritional well-being.

How price and non-price factors influence the market price of major carp: Findings from the dynamic ARDL approach

Recently, fish prices in Bangladesh have been growing, particularly the market price of major carp, even though the factors that may be contributing to this trend are unexplored. Therefore, this study considers some price and non-price factors to explore the market price dynamics of major carp species in Bangladesh. To explore the factors impacting the market price of carp fish, we apply advanced time-series econometric modeling, such as the recently devised dynamic simulated autoregressive distributed lag (ARDL) model using publicly available data on fish prices from 2005 M1 to 2021 M12. This period is attributed to the rapid transformation from mostly low-intensity farming to commercialization due to a rise in productivity and market price. Among the price factors, the findings demonstrate that the price of corn, soybean, and oilcake, and fisheries wage rate have a positive significant impact on fish price in both the short and long term. In addition, among the non-price drivers, GDP per capita, inflation rate, and fish consumption all have a significant positive influence on fish prices in the long run, whereas total production has a detrimental effect on fish prices in the long run. These results have significant policy ramifications for attaining the fish farmers' profit maximization and consumers' welfare maximization goals.

Macroeconomic Determinants of Renewable Energy Production in Jordan

Using the years 1970–2022, this research looks at the macroeconomic variables that have an impact on Jordan's renewable energy production. To assess the main factors impacting Jordan's renewable energy scenario, the study employed the Dynamic Autoregressive Distributed Lag (DARDL) tool. Several important aspects affecting renewable energy production are highlighted in the study. A multitude of factors that statistically and positively impact the attainment of renewable energy are diverse economic activities as measured by the GDP. The contribution of a fair economy in speeding up and maintaining promotion of renewable energy projects in Jordan is noticeable. Moreover, FDI is one of the factors in fostering renewable energy production and its role is very significant and positive, thus, it is affecting renewable energy production in a great extent. In a nutshell, overseas funding should be meant to yield a great harvest in planting and constructing Jordanian renewable energy plants and equipments. The study also indicates that finance is not just fundamental for renewable energy movement but for the evolution of the renewable energy industry as well. While the tried and true fact of the positively and statistically significant case of link between renewable energy production and financial development underline the vital role of a financially prosperous area in redirecting investments from traditional resources to renewable energy and therefore contribute to its further expansion. However, that idea needed some modification due to the disagreement that the CO2 emissions also have significant role in reducing renewable energy production. The study has identified that there is a negative correlation between the carbon emissions and renewable energy production if it is computed statistically. The implementation of more effective policies and initiatives that will result in reduced carbon emissions becomes possible with all sectors working together to form a better environment for the establishment of renewable energy infrastructures.

Modelling the determinants of birth tourism demand: a time series analysis

ABSTRACT Birth tourism has been steadily rising in recent years as potential mothers travel across borders with the main purpose of giving birth. However, the economic reasons behind this form of tourism have not been adequately examined in the existing literature. This study explores the determinants of birth tourism by incorporating factors such as the average income of visitors, price level, exchange rate, and infant mortality while accounting for the potential impact of the Citizenship Amendment Act 2005 in New Zealand. The variables have been introduced into the model using a standard tourism demand framework and a new dynamic autoregressive distributed lag method has been used in the study. Ultimately, rising income of visitors and exchange rate are reported to boost birth tourism while rising price reduces birth tourism. The result also shows that the implementation of the Citizenship Amendment Act 2005 has no significant effect on birth tourism, which implies that monetary and financial considerations such as the income of tourists, price level, and exchange are the most important drivers of birth tourism in New Zealand.

Do clean energy and dependence on natural resources stimulate environmental sustainability? A new approach with load capacity factor and temperature

AbstractIndia, not being an exception from the rest of the globe, has also been suffering from the unprecedented challenges of survival of the ecosphere, which is highly threatened by a continuous weakening of the environmental quality as a result of accelerated accumulation of anthropogenic emissions in the biosphere. As an immediate aftermath of the famous Glasgow Conference of Parties (COP26), while the Government of India is aiming to find realistic solutions to achieve net zero emissions goals by 2070 without much confrontion with its ambitious economic progression goals, accordingly proposes the alternative pathway to achieve environmental sustainability goals, this study has considered one of the three pivotal drivers of economic sustainability in the modern era of digitization, that is, natural resources rent, renewable energy consumption, and economic growth and examined their role on rendering environmental sustainability. Instead of CO2 emissions, load capacity factor and temperature have been considered proxies of environmental quality. This study has employed a novel dynamic autoregressive distributed lag (ARDL) estimation technique to portray the short‐ and long‐run elasticity of environmental quality indicators by considering 48 years of annual time series data from 1970 to 2017. The findings of this study have documented that while natural resources rent and economic growth stimulate environmental squalor, renewable energy consumption promotes environmental sustainability. This study also unveiled different causal relationships using the frequency domain causality analysis. Subsequently, this study designs some vital policy measures and integration of which in the existing energy‐climate framework may assist Indian policymakers in achieving the net zero climate goal.

Energy security-related risks and the quest to attain USA’s net-zero emissions targets by 2050: a dynamic ARDL simulations modeling approach

The Russia-Ukraine war and other similar conflicts across the globe have heightened risks to the United States of America's (USA’s) energy security. However, little is known about the severity of the effect of energy security risks on the USA’s quest to attain net-zero emissions targets by 2050. To this end, we examine the effect of energy security risks on the load capacity factor (LCF) in the USA. Employing a time series dataset spinning from 1970 to 2018, the results of the Dynamic Autoregressive Distributed Lag (ARDL) simulations model suggest that energy security-related risk hampers the long-term net-zero emissions targets with its effect decreasing over time until it varnishes in about 5 years time. The results also show that foreign direct investment (FDI) inflows, renewable energy consumption, and green technology have long- and short-run positive effects on the LCF. Conversely, economic expansion and urbanization impede environmental quality by lowering the LCF both in the long run and short run. These findings are upheld by the outcomes of the multivariate quantile-on-quantile regression. Therefore, the study advocates for the consumption of renewable energy, investment in green technologies, and FDI inflows to mitigate energy security-related risks and attain the net-zero emissions targets by 2050 in the USA.

Analysing the nexus between clean energy expansion, natural resource extraction, and load capacity factor in China: a step towards achieving COP27 targets

AbstractThe excessive use of non-renewable energy in 21st-century economic growth has continued to hurt the environment by accumulating carbon dioxide and other greenhouse gases. However, promoting environmental sustainability requires expanding clean energy utilisation. In this study, we examine the effects of clean energy expansion and natural resource extraction on load capacity factor (LCF) in China from 1970 to 2018. Using the dynamic autoregressive distributed lag simulations approach, we extend the standard load capacity curve (LCC) hypothesis by incorporating clean energy expansion and natural resource extraction as main determinants of the LCF. The empirical outcomes reveal that economic expansion is, although positively associated with the LCF, but its squared term degrades the LCF. This confirms that the LCC hypothesis is not valid for China. Moreover, while clean energy expansion has a positive effect on the LCF, the effect of natural resource extraction is negative. These effects are stronger and statistically significant only in the long run. Therefore, this study highlights the potentials for a sustainable decarbonized economy in China by investing not only in clean energy sources but also efficiently use the available natural resources in the country.

The dynamic impact of oil shocks on the Saudi stock market: new evidence through dynamic simulated ARDL approach

PurposeThe paper investigates the dynamic relationship between oil prices, the USA dollar exchange rate and the Saudi stock market index.Design/methodology/approachThe authors perform a novel dynamic simulated the autoregressive distributed lag (ARDL) on weekly data from 2010 to 2021.FindingsThe authors' work reveals three main results: First, a cointegration relationship exists between oil prices and the Saudi stock market index. Second, the Saudi stock market is strongly affected by fluctuations in oil prices in both the short and long run. Third, the exchange rate of the USA dollar has a slight influence on the movements of the Saudi stock market. The simulations show that the Saudi stock market index has a long-run upward trend after an oil price shock, while the dollar index rises moderately after a similar shock. Moreover, the first months of the COVID-19 pandemic coincided with a significant decline in the Saudi stock market index, particularly the substantial drop in oil prices.Practical implicationsThese findings encourage domestic and foreign investors to benefit from an upward trend in oil prices, especially after the opening of the Saudi market to foreign investment. On the other hand, it raises questions about the Saudi economy's dependence on oil as the sole vehicle for output growth. It highlights the urgent need for diversification and productivity growth in the non-oil sector and other renewable natural resources to increase Saudi competitiveness.Originality/valueThe novelty of the research lies in the following. First, the authors apply one of the latest developments in time-series modeling techniques. This dynamic ARDL simulation model provides a worthwhile alternative way to explore dynamic correlations in the short and long run and assess the choc effects. Secondly, the study would enable us to track the impact of the COVID-19 health crisis on the Saudi stock market.

The electrification-social development nexus in Bangladesh: Do governmental policies matter?

Electrification can advance sustainability and social development. This study investigates the contribution of electrification to various aspects of societal development, i.e., educational development, human development, and gender inequality, within the purview of economic globalization, economic growth, and governmental policies in Bangladesh, covering the data during 1990–2019. Employing the dynamic autoregressive distributed lag (DARDL) simulation approach, we have established a positive impact of electricity on the decomposed parameters of social development in Bangladesh. Finally, we employ the DARDL simulation-based counterfactual analysis procedure, testing the effects of 1% and 5% (±) shocks to the regressors and their effects on the dependent variables.

Nexus of green energy innovation, governance quality, and CO2 emissions in natural resource sector: The role of sustainable human development

Majority of the literature suggest that the determination of natural resources as either a curse or a blessing cannot be solely based on their impact on economic and social development. This study investigates the relationship between sustainable human development, governance quality, green energy innovation, and CO2 emissions in oil and gas sector of China. The data from 2002 to 2022 was taken for the analysis. This study employed the novel dynamic autoregressive distributed lag (D-ARDL) method to examine long-term and short-term relationships. Our results of short-term analysis demonstrated that all the variables – institutional governance, sustainable human development renewable energy innovation, government stability and renewable energy consumption–have an influential role in reduce carbon emissions. The results of long run analysis reiterate findings of short run, highlighting the inverse relationship of the variables with carbon emissions. The t-statistic shows of such relationships are statistically significant in the long run. The findings of the study offer valuable insights and empirical evidence to reduce emissions using governance quality, sustainable human development, green energy innovation and renewable energy consumption in oil and gas sector of China. Our findings demonstrate that implementation of these strategies require long-term commitment and concerted efforts.

Mineral import demand-driven solar energy generation in China: A threshold estimation using the counterfactual shock approach

The global shift towards clean energy sources necessitates a substantial supply of critical minerals to drive renewable energy technologies such as concentrating solar-thermal power (CSP) and solar photovoltaics (PV). This growing demand for mineral resources underscores the need to examine the import-demand function of minerals in relation to solar energy generation in China, spanning the period from 1996 to 2019. By employing both conventional autoregressive distributed lag (ARDL) and dynamic ARDL (DARDL) simulation methods, our study reveals a positive and significant long-term relationship between mineral import demand and installed solar capacity. Furthermore, our analysis demonstrates that the average mineral price elasticity adheres to the Marshallian demand premise, while the substitution effect between mineral prices and oil prices contradicts this premise. Conversely, the impact of exchange rates on mineral import demand is found to be negligible. However, our findings indicate that long-term economic growth fosters mineral import demand. To ensure the robustness of our results, we conduct a dynamic ARDL simulation-based counterfactual analysis to measure the threshold level of mineral response to (±) shocks in the regressors, including oil and mineral prices, exchange rates, and GDP growth. These findings provide valuable insights for policy recommendations in China, emphasizing the importance of maximizing mineral resources to augment solar power production and achieve the global objective of a carbon-neutral landscape in the twenty-first century.

Modeling the impacts of changes in nuclear energy, natural gas, and coal in the environment through the novel DARDL approach

There is no doubt that energy consumption and production are the primary sources of greenhouse gases that contribute to climate change and global warming. It will be extremely helpful to develop policies to reduce carbon dioxide emissions by analyzing and estimating the factors responsible for these harmful gases. Against this background, the purpose of this study is to perform an analysis of energy services at a disaggregated level in the environment using a novel methodology called Dynamic Autoregressive Distributed Lag (DARDL) for Pakistan utilizing time series data from 1975 to 2021. Further robustness check is performed by Kernel-based regularized least squares (KRLS). Results show that (i) energy indicators and CO2 emissions are cointegrated; (ii) there is a statistically significant effect of nuclear, hydro, natural gas, oil, and coal energy use on CO2 emissions; (iii) nuclear energy reduces CO2 emissions; (iv) Nuclear energy dwindles CO2 emissions to a great extent when a positive shock is applied to it, even if it is 300%; (v) The use of hydro energy also declines CO2 emissions; (vi) Further, CO2 emissions increases dramatically with positive shocks to coal, even if they are 25 %; and, the robustness of the results is verified through KRLS method. This implies that energy consumption is a significant determinant of CO2 emissions in Pakistan, so efforts to reduce CO2 emissions should be considered in order to ensure a sustainable future.

Race to carbon neutrality in South Africa: What role does environmental technological innovation play?

Governments worldwide have prioritized carbon reduction and neutrality to address the escalating threat of climate change. These goals are in line with the United Nations Sustainable Development Goals (UNSDG-13). These goals stress taking action on climate change to lessen the bad effects of human activities and using fossil fuels for energy. To this end, the present study investigates the connection between conventional energy usage, agricultural practices, economic growth, and their impact on environmental sustainability in South Africa. Additionally, it explores the role of renewable energy consumption and environmental technological innovation in mitigating these effects. To achieve the study objectives, a carbon-income function is fitted with an annual frequency data from 1975 to 2020. The present study leverages on Pesaran's Autoregressive distributed lag (ARDL) method and for robustness analysis the dynamic ARDL simulations method to simultaneously explore the short and long-run coefficients of the study's outlined variables. Empirical analysis, confirmed by bounds testing for cointegration, reveals a long-term equilibrium relationship among the variables considered. Notably, economic growth, fossil fuel energy consumption, and agricultural activities have adverse effects on environmental sustainability in South Africa, indicating a trade-off between economic growth and environmental quality. Dynamic ARDL simulations provide further evidence of an Environmental Kuznets Curve (EKC) phenomenon. However, renewable energy consumption and environmental technological innovation positively influence environmental quality. These findings highlight the imperative for South Africa and its stakeholders to adopt green growth policies and transition to cleaner energy alternatives.

Is agricultural production detrimental to Greece's ecological sustainability? Evidence from the dynamic ARDL simulations and bootstrap causality analysis

AbstractAgricultural production (AP) can play a prominent role in establishing food security. However, AP has an innate relationship with nature's demand and supply sides. Furthermore, unsustainable AP can harm a country's ecological sustainability by increasing the ecological deficit (i.e., the disparity between ecological footprint and biocapacity). In that respect, this research econometrically examines the AP–ecological deficit link in Greece for the 1965–2018 period. The following findings are revealed in this study. First, the ecological deficit and AP in Greece have a cointegrating relationship. Second, besides energy use and income, AP significantly contributes to Greece's ecological deficit in the long run. Third, the dynamic autoregressive distributed lag simulations show that the short run effect of a positive (negative) shock in AP on Greece's ecological deficit is insignificant. However, in the long run, the impact of a positive (negative) shock in AP gradually becomes significant and intensifies (decreases) the ecological deficit in Greece. Last, the bootstrap causality test with the Fourier approximation generally confirms these findings, revealing that unidirectional causality runs from AP to the ecological deficit. These results indicate that unless sustainability measures are strictly followed in the energy and agricultural sectors, AP could continue undermining Greece's long‐run ecological sustainability.

Biodiesel and its potential to mitigate transport-related CO2 emissions

Many studies have concentrated on the energy capacity of biodiesel to reduce CO2 emissions at the aggregate level and not much at the sectoral level. This study addresses this gap and attempts to estimate the impact of the use of palm biodiesel on the transport CO2 emissions in Malaysia during 1990–2019. It also predicts the impact of implementing the B10 blending program (10% biodiesel in diesel fuel) on CO2 emissions from transport in this country. For this purpose, this study uses the dynamic autoregressive distributed lag (ARDL) and Kernel-based regularized least squares. This model can plot and estimate the possible actual changes in biodiesel consumption to predict its impacts on transport CO2 emissions. The results suggest that a one-way Granger causality exists from transport GDP, diesel consumption, and motor petrol consumption to palm biodiesel consumption. An increase of 1% in the use of biodiesel reduces carbon emissions from road transport by 0.004% in the long run, while, in the short run, it is associated with a 0.001% increase in transport CO2 emissions. The simulated results from the dynamic ARDL model suggest that a 10% increase in the share of biodiesel consumption in fuel transport by 2030 would reduce the rate of the increase in road transport carbon emissions. The improvement and management of new technologies in oil palm plantation and harvesting can help increase palm oil production for biofuels and edible oil and to reduce forest replacement and therefore biodiversity and food security.

Assessing the Economic Impacts of Tourism Markets and Activities Diversification: Evidence From a New Dynamic Regression Approach

This paper undertakes an exploration of how the diversification of tourism markets and activities exerts its influence on economic growth, particularly in New Zealand. By employing a dynamic autoregressive distributed lag method, this study uncovers the interplay between tourism diversification and economic growth dynamics in New Zealand. Empirical results show that it is only the diversification of European markets that generated positive economic growth. However, the diversification of tourism activities emerges as a potent driver of favorable economic expansion. The evidence suggests that a strategic emphasis on augmenting tourism diversification from European markets holds the potential to magnify the positive economic impact of the tourism sector. Additionally, the proposition of enhancing diversification in tourism activities emerges as a key avenue for bolstering New Zealand’s economic growth prospects. This study bridges a gap in the existing literature and furnishes policymakers with insights on how to harness the potential of tourism diversification.

Balancing the scales: Does public debt and energy poverty mitigate or exacerbate ecological distortions in Nigeria?

AbstractAmid Nigeria's economic growth and energy challenges, the escalating public debt (PB) levels and persistent energy poverty (EP) raise critical questions about their potential impacts on the environment. Given the potential conflict among economic development, EP alleviation and ecological conservation, it becomes pertinent to understand whether increased PB and efforts to address EP inadvertently contribute to or alleviate ecological imbalances within the country. Hence, this research investigates the effect of PB and EP on the load capacity factor (LCF) in Nigeria. Using the STIRPAT model and annual data from 1990 to 2021, the study explores the relationships among total PB, EP, gross domestic product per capita, urbanization and LCF. Descriptive analysis, correlation assessments and unit‐root tests precede the data analysis conducted with the autoregressive distributed lag (ARDL) model and dynamic ARDL (DARDL) technique. Key findings reveal significant negative effects of urbanization and EP on LCF. Additionally, the ARDL and DARDL procedure highlights a positive long‐term relationship between PB and LCF. Both ARDL and DARDL analyses show a negative short‐term relationship between GDP growth per capita and LCF, signalling the need for sustainable economic practices. The study concludes with policy recommendations that aim to promote sustainable development and address ecological imbalances by tackling EP and PB challenges in Nigeria.

Does climate policy uncertainty affect carbon emissions in China? A novel dynamic ARDL simulation perspective

This study provides new insights into the impact of climate policy uncertainty, energy consumption, and economic development on China’s carbon emissions. In doing so, we develop a novel index of China’s climate policy uncertainty (CCPU). We then use the newly constructed dynamic autoregressive distributed lag (ARDL) simulation model, the frequency-domain causality (FDC) test, and the fully modified OLS (FMOLS) estimation to investigate these potential relationships from 2005 to 2021. The empirical results suggest that increasing CCPU reduces carbon emissions in most parts of China, which improves environmental degradation. Furthermore, the effects of energy consumption and economic growth on carbon emissions are confirmed to be positive in each location. Finally, the results of the FDC and FMOLS confirm the robustness of the model. Our findings suggest that information from the CCPU can be used to forecast CO2 emissions in China. Furthermore, the government should strike a balance between economic growth and environmental regulation and promote the use of renewable energy to reduce carbon emissions. Proactively developing climate policy is important to achieve the goal of carbon neutrality.