The intricate relationship between economic growth, environmental quality, and energy consumption has been extensively debated and studied on a global scale. The impacts of ecological quality on economic growth have been observed to be both positive and negative, particularly about human health as a result of pollutant emissions. It is essential to examine the compatibility between economic growth and environmental improvement, particularly through the reduction of emissions. This study aimed to investigate the connection between economic growth in forested areas and the corresponding impact on carbon dioxide (CO2) emissions in Nepal (Rose and Fisher, 1970). The analysis utilized time series data from 1990 to 2020, employing the dynamic ordinary least squares (DOLS) method. The DOLS results demonstrated a positive and statistically significant relationship between economic growth and CO2 emissions (Shafik and Bandyopadhyay, 1992). Specifically, an increase of Rs. 10 million in gross domestic product (GDP) corresponded to a 0.6112 kiloton increase in CO2 emissions. In contrast, the long-term coefficient for forested areas exhibited a substantial association, indicating that a reduction of one square kilometer of forested area (deforestation) resulted in an increase of 68.37 kilotons in CO2 emissions in Nepal. These findings accentuate the divergent effects of economic progress and deforestation on carbon emissions in Nepal, with GDP growth contributing to a greater increase in emissions. Therefore, the implementation of effective strategies and economic measures, such as afforestation and reforestation, forest protection, sustainable forest management, and mechanisms like REDD+ (reducing emissions from deforestation and forest degradation plus), can play a vital role in mitigating carbon emissions while simultaneously addressing deforestation and ensuring long-term economic progress in Nepal.

1. Introduction

In contemporary environmental economics literature, a significant focus lies in examining the pragmatic linkages between pollution and economic growth. The emphasis has gained importance since the early 1990s, coinciding with growing concerns about climate change, notably global warming, decreasing from deteriorating environmental quality. The substantial contribution of carbon dioxide (CO2) emissions is of particular concern, widely recognised as a primary driver of global warming [1]. Consequently, a substantial body of literature exists dedicated to exploring this complex relationship. Recent studies continue to examine various aspects of this connection, casting light on its complexities and implications for sustainable development worldwide [2–4].

Economic growth has long been the central focus for policymakers in pursuit of sustainable development. Still, since the late 1990s, environmental quality has gained recognition as crucial for sustainability, corresponding with global summits like Kyoto, Johannesburg, and Rio de Janeiro. However, economic growth often conflicts with environmental goals, mainly through energy consumption. Policymakers are challenged to balance these conflicting objectives. Energy economics literature extensively explores the interplay between economic growth, energy use, and environmental quality, reigniting discussions about their implications [5–7].

The complex interplay between economic growth, environmental quality, and energy consumption has long been a subject of debate and academic research worldwide [8]. Ecological quality can both positively and negatively impact economic growth, particularly through its effects on human health from pollutant emissions. Policymakers face conflicting goals in balancing economic growth with environmental preservation. Understanding this relationship is crucial for shaping effective energy and environmental policies [3, 9, 10].

The detrimental impacts of climatic risks on human well-being and socio-economic stability emphasise climate change’s significant threat to global economic prosperity. Effective justification of the risks mentioned above and mitigation of inevitable losses and damages necessitates accurately forecasting future climatic patterns, particularly greenhouse gas emissions, and understanding socio-economic development and equity trajectory. These aspects are crucial for informed decision-making and the formulation of resilient adaptation strategies [3, 4, 11].

Examining the compatibility between economic growth and environmental enhancement, particularly through emission reduction, is crucial [12]. Chen and Liu [13]; and Shahbaz, Balsalobre Lorente, and Sharma [14] noted a rise in oil prices, sluggish economic development, and environmental regulations in the United States. The relentless pursuit of economic expansion, especially regarding natural resources like forests, puts significant pressure on them. Deforestation, a major contributor to carbon emissions after burning fossil fuels [15], has led to changes in global land use and decreased forest coverage. Practices such as wood harvesting for agriculture contribute to atmospheric carbon emissions. Despite their scarcity, natural resources are vital for development and manufacturing [16].

The study of Saidi and Rahman [17] highlighted that over the long term, there are reciprocal causal connections between gross domestic product (GDP) and energy usage in all nations. Similarly, bidirectional causal links between GDP and CO2 emissions are evident in all countries except Algeria. Likewise, mutual causal relationships are observed between energy consumption and CO2 emissions across all countries except Venezuela, where a unidirectional causality from CO2 to energy consumption is identified. Environmental degradation is inevitable when the interconnection between natural resources and modern development processes persists [17, 18]. Given the universal pursuit of economic growth, understanding the impact of economic expansion on the environment is crucial [19, 20]. Numerous studies have explored the intricate relationship between environmental quality and economic growth in various countries, offering empirical insights. Over the past 2 decades, researchers have increasingly demonstrated the linkages between CO2 emissions, economic growth, and energy usage [21–23].

Protecting the environment and promoting sustainable development pose global challenges, especially in developing nations where economic growth conflicts with reducing carbon emissions [24]. Governments worldwide rely on environmental regulations to ensure a healthy ecosystem while fostering economic progress [25, 26]. Yet, existing research often overlooks the impact of environmental regulations on farmers’ income, focusing mainly on industrial pollution [24]. With environmental governance expanding to agriculture, scholars should prioritise studying the unique characteristics of agricultural production in environmental research [24, 27].

Perman and Stern [28] and Apergis and Ozturk [29] investigated the Environmental Kuznets Curve (EKC) hypothesis using panel data from 74 nations in the context of the inquiry into the compatibility between increased economic growth and environmental improvement. They applied a panel cointegration technique, examining per capita Sulfur oxide emissions concerning per capita income and its squared term. Contrary to the EKC theory, their analysis revealed no evidence supporting it. The EKC theory suggests that environmental degradation initially worsens during the early stages of economic growth but improves as the economy expands, depicting a curvilinear relationship between economic growth and environmental quality [21, 30]. In examining Nepal, the primary focus is understanding the impact of CO2 emissions, GDP, and forest area.

1.1. Hypothesis

H0: There is no significant relationship between economic growth in forested areas and their respective impact on Nepal’s CO2 emission.
H1: There is a significant relationship between economic growth in forested areas and their respective impact on Nepal’s CO2 emission.

1.2. Theoretical Foundation of the Study

Political Ecology theory offers valuable perspectives for understanding the relationship between economic growth in Nepal’s forested areas and its impact on carbon emissions. Political Ecology highlights how socio-political factors like economic policies and power dynamics influence deforestation rates. In contrast, environmental sociology investigates how communities perceive and respond to development initiatives that impact carbon emissions. Similarly, Sustainable Development Theory seeks to balance economic growth with environmental conservation, assessing the sustainability of Nepal’s strategies [31, 32]. Institutional Theory examines the role of government policies in managing forests and mitigating emissions. Economic Theory, including the EKC, explores the link between economic growth and environmental degradation. Participatory Action Research involves collaboration with local communities to design interventions for sustainable development, considering their perspectives and experiences [33].

2. Literature Review

The relationship between CO2 emissions and trade and industry expansion has been extensively studied across different industrialized and developing nations, including BRIC nations [34], MENA nations [35, 36], OECD nations [37], industrialized countries [38], Jorgenson and Wilcoxen [39] and small-economy states [40]. Similarly Ajmi et al. [41]; examine the relation between energy, CO2 emissions and GDP with S-dimensional vector autoregressive model for G7 countries. In another way Li et al. [42] applied multiple regression models to examine how different biological variables directly and indirectly affect tree carbon storage. This demonstrates that different methods can be employed for the analysis of both CO2 emissions and econometric variables using various ways such as time series data. This shows different methods can be applied to study CO2 emissions and econometric variables using different methods for time series data.

These studies, particularly those on the EKC, aim to balance environmental concerns like pollution or emissions with economic growth. The EKC hypothesis posits a U-shaped curve, indicating an initial increase in environmental degradation with economic development followed by a decrease [14, 43]. However, conflicting findings also suggest alternative curves, such as an inverted N-shape or an absence of correlation between CO2 emissions and GDP per capita [44, 45].

Studies examining specific countries or regions provide insights into the nuances of this relationship. For instance, Shahbaz, Lean, and Shabbir [46] and Al-Mulali et al. [47] explore the connections between Pakistan’s CO2 emissions, energy usage, economic improvement, and trade openness, finding support for the EKC hypothesis when traditional GDP variables are combined with energy consumption and trade openness. Similarly, Leitão [48] investigates energy use and foreign direct investment (FDI) in Portugal, confirming the EKC assumptions with variables like per capita income and per capita squared income.

However, conflicting findings also emerge from different studies. Soytas and Sari [49] find no long-run causal link between carbon emissions, energy use, and GDP, while Akbostanci et al. [50] and Halicioglu [51] present evidence contrary to the EKC hypothesis using Turkish data.

The role of forests in carbon sequestration and emissions is also explored. Taeroe et al. [52] find that forests can reduce CO2 emissions more than wood utilized as an energy source. Ahmad et al. [53] highlights the significant contribution of deforestation and forest degradation to carbon emissions in Pakistan’s Himalayan region.

Energy consumption, a major contributor to carbon emissions, is analyzed in the context of economic growth. Esso and Keho [54] and Liu et al. [26] find a long-term relationship between economic growth and carbon emissions in African nations, with increasing economic growth generally associated with lower carbon emissions. Salahuddin, Shabbir and Ozturk [55] analyze data from GCC nations, revealing a bidirectional causal relationship between monetary expansion and carbon emissions.

Studies also investigate the relationship between economic growth, financial development, and environmental performance. Tamazian and Bhaskara Rao [56] and Tamazian, Chousa and Vadlamannati [57] suggest a positive association between financial development and environmental quality. However, conflicting findings are reported by Jalil and Feridun [58]; Sadorsky [59] and Zhang and Cheng [60]; emphasizing the complexity of this relationship.

Recent studies have applied innovative methodologies to explore the dynamics between economic growth, environmental preservation, and government policies. For example, Işık and Çelik [61] introduce a composite model to reevaluate the EKC hypothesis for U.S. states, while (Dam, Işık and Ongan [62] pioneer the integration of the Armey curve hypothesis with the EKC to examine the impact of government expenditures on CO2 emissions in USMCA countries.

2.1. Summary of the Findings From Previous Studies

The empirical studies of Ahmed et al. [63]; Shan et al. [64]; Hao et al. [65]; Tongwane and Moeletsi [66]; Nathaniel and Khan [67]; Khan, Hou and Le [8]; Zhou, Tang and Zhang [68] Akram et al. [69]; Rahman [70] and Nasir, Canh and Lan Le [71] on the relationships between energy variables, environment, and economic growth highlight that economic growth, urbanisation, and industrialisation consistently contribute to environmental degradation and CO2 emissions, while factors such as human capital, green growth policies, renewable energy adoption, and energy efficiency measures show potential for mitigating environmental impacts. Moreover, the studies above highlight the complex interplay between economic variables, energy consumption, and environmental quality, challenging simplistic models (see Table 1).

Table 1. Summary of the previous studies on the relationships between energy variables, environment and economic growth.

Authors and years	Objectives	Methods	Findings	Research gap
Ahmed et al. [63]	It aims to investigate the impact of natural resource abundance, human capital, and urbanisation on China’s ecological footprint, focusing on controlling economic growth	Bayer and Hack cointegration test and bootstrap causality technique	The findings demonstrate a long-term equilibrium relationship, with natural resource rent increasing the ecological footprint and urbanisation and economic growth contributing to environmental degradation, while human capital mitigates deterioration	The main research gap in this study lies in its limited focus on examining the potential interaction effects of natural resource abundance, human capital and urbanisation on ecological footprint, particularly in controlling economic growth
Shan et al. [64]	It aims to construct the most recent CO2 emission inventories for China and its 30 provinces and their energy inventories for 2016 and 2017	The administrative-territorial scope outlined by the Intergovernmental pane	The results show that newly compiled CO2 emission inventories provide crucial updates and supplements to the dataset, enabling a comprehensive analysis of emission patterns and their regions, which serve as valuable inputs for climate and integrated assessment models, aiding in formulating effective carbon policies	The study constructs CO2 emission inventories for different administrative regions but lacks analysis on specific sectors or industries driving CO2 emissions within these regions. While aiming to provide comprehensive inventories, it overlooks sectoral breakdowns and variations in emission patterns among different economic activities
Hao et al. [65]	It aims to investigate the role of environmentally adjusted multifactor productivity growth (green growth) on CO2 emissions for G7 countries from 1991 to 2017	The cross-sectionally augmented autoregressive distributive lag (CS-ARDL) model	The study’s results demonstrate that various aspects of green growth, including both linear and nonlinear terms, decrease CO2 emissions. Furthermore, environmental taxes, human capital and adopting renewable energy contribute to this reduction	Hao et al. [65] fail to analyse specific sectors or industries within G7 countries that significantly contribute to CO2 emissions, thereby overlooking a crucial aspect. Despite aiming to examine the impact of green growth on reducing CO2 emissions, the study lacks exploration into sectoral emissions breakdown and potential variations in emission patterns across different economic activities within these countries
Tongwane and Moeletsi [66]	The aim is to offer insights into emissions’ growth, stability and decline across various global regions from 1990 to 2018	A literature review to analyse GHG emissions trends in the specified economic sectors	The findings reveal limited global efforts to reduce GHG emissions, with Europe and North America showing partial decarbonisation progress while rapidly industrialising regions, notably Eastern Asia, southern Asia, and south-East Asia, continue to expand fossil-based energy systems, alongside an increase in AFOLU emissions in Latin America due to agriculture expansion into carbon-dense tropical forest areas	The research gap in Tongwane and Moeletsi [66] lies in the lack of detailed analysis regarding specific economic sectors contributing significantly to GHG emissions across various global regions. While the study aims to provide insights into emissions trends, it does not examine sectoral breakdowns or explore potential differences in emission patterns among different economic activities within these regions
Nathaniel and Khan [67]	It aims to investigate the influence of renewable and nonrenewable energy consumption, economic growth and urbanisation on environmental degradation in ASEAN countries	First- and second-generation unit root and cointegration tests	The study reveals that economic growth, trade and nonrenewable energy consumption exacerbate environmental degradation in ASEAN countries, highlighting the environmental costs of economic development and the influence of urbanisation on energy consumption	The research gap in Nathaniel and Khan [67] involves the absence of detailed analysis on specific sectors or industries in ASEAN countries significantly impacting environmental degradation, despite the study’s aim to explore various factors influencing this phenomenon, omitting the sectoral breakdown of emissions and potential variations in emission patterns across different economic activities
Khan, Hou and Le [8]	To analyse the relationships among variables and their long-term effects on environmental quality using structural break Zivot-Andrews and breakpoint ADF unit-roots tests for stationary analysis	The generalised method of moments (GMM), generalised linear model (GLM), and robust least-squares	The findings indicate that natural resources and renewable energy consumption negatively influence the ecological footprint and CO2 emissions. In contrast, nonrenewable energy consumption, population growth and biocapacity positively affect these environmental indicators, with bidirectional causality between natural resources and CO2 emissions and between natural resources and the ecological footprint, and unidirectional causality from population growth to energy consumption, the ecological footprint, and CO2 emissions	The research gap in Khan, Hou and Le [8] lies in the lack of detailed analysis regarding specific sectors or industries within the studied context that significantly contributes to environmental quality. While the study aims to analyze the relationships among variables and their long-term effects on environmental quality, it does not delve into the sectoral breakdown of emissions or explore potential differences in emission patterns among different economic activities within the studied area
Zhou, Tang and Zhang [68]	To assess green finance’s influence on economic development and environmental quality	The global principal component analysis to establish a green finance development index	The analysis reveals that the development of green finance positively contributes to economic growth. Additionally, green finance demonstrates a favourable impact on environmental quality, albeit with variations across different levels of economic development	The research gap in Zhou, Tang and Zhang [68] lies in the limited exploration of how the impact of green finance on environmental quality varies across different levels of economic development
Akram et al. [69]	To investigate energy efficiency’s role in analysing its impact alongside renewable energy (RE) and other factors on carbon emissions in developing countries	Panel ordinary least squares and fixed-effect panel quantile regression (PQR) techniques	The study reveals that energy efficiency consistently decreases CO2 emissions, especially at the 90th quantile. In contrast, renewable energy notably reduces emissions, particularly at the 10th quantile, with further distinctions in the influence of nuclear energy and GDP across quantiles, ultimately supporting the environmental Kuznets curve hypothesis in developing nations	The research gap in Akram et al. [69] is the limited exploration of the intricate relationships and potential synergies among energy efficiency, renewable energy, and other factors influencing carbon emissions in developing countries, especially within different quantiles, which could provide deeper insights into effective mitigation strategies
Rahman [70]	To investigate the impact of electricity consumption, economic growth, and globalisation on CO2 emissions in the top 10 electricity-consuming countries	Fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methods	Empirical findings highlight the interconnectedness of electricity consumption, economic growth, globalisation, and CO2 emissions in the top 10 electricity-consuming countries, indicating that while electricity consumption and economic growth positively impact CO2 emissions, globalisation has a significant negative effect, implying potential improvements in environmental quality	The research gap in Rahman [70] lies in the lack of detailed exploration into the specific mechanisms through which electricity consumption, economic growth, and globalisation interact to influence CO2 emissions in the top 10 electricity-consuming countries
Nasir, Canh and Lan Le [71]	To investigate the factors influencing environmental degradation in Australia, focusing on the impact of economic growth, trade openness, industrialisation, energy consumption, and financial development on CO2 emissions from 1980 to 2014	The STIRPAT model	Empirical findings demonstrate short-term bidirectional causality between economic growth, energy consumption, industrialisation, and stock market development with CO2 emissions, while no significant evidence supports the environmental Kuznets curve hypothesis; moreover, the study underscores the long-term positive impact of financial development, energy consumption and trade openness on CO2 emissions, with industrialisation surprisingly showing no significant effect	The research gap identified by Nasir, Canh and Lan Le [71] lies in the lack of significant evidence supporting the environmental Kuznets curve hypothesis, suggesting further exploration into the relationship between industrialisation and CO2 emissions in Australia

The results highlight the findings of Ahmed et al. [63]; Shan et al. [64]; Hao et al. [65]; Nathaniel and Khan [67]; Khan, Hou, and Le [8]; Zhou et al. [68]; Akram et al. [69]; Rahman [70] and Nasir et al. [71] indicate a positive correlation between energy consumption and economic expansion but Tongwane and Moeletsi [66] potentially found a negative relationship between energy variables, the environment, and economic growth. However, without access to the specific findings of each study, it is challenging to state their conclusions definitively.

2.2. Research Gap

The findings emphasise different critical aspects of global environmental challenges and policy responses. Firstly, there is a concerning disparity in global efforts to contest greenhouse gas emissions, with regions like Europe and North America showing progress while rapidly industrialising areas continue to rely heavily on fossil fuels, worsening climate change. Secondly, insights from ASEAN countries reveal the environmental costs of economic development, where growth, trade, and nonrenewable energy consumption worsen environmental degradation, necessitating sustainable development strategies that balance economic growth with environmental protection. Thirdly, the studies reveal elaborate causal relationships between energy consumption, economic growth, and CO2 emissions, emphasising the need for further research to clarify the above-highlighted dynamics and inform effective environmental policies. Lastly, the findings highlight the importance of quantitative analysis in evaluating policy effectiveness, urging continued research to identify optimal strategies for achieving global sustainability goals through measures like green finance, renewable energy adoption, and environmental taxes. Most empirical studies were conducted in developed countries (see Table 1). Still, Nepal is an underdeveloped country, so new research is emerging in Nepalese to understand the relationship between economic growth and environmental enhancement, mainly through emission reduction.

3. Materials and Methods

This study investigated an empirical analysis of the dynamic impacts of economic growth and forested areas on CO2 emissions in Nepal by using the DOLS cointegration approach. We chose this approach because DOLS is a valuable econometric method for analyzing time series data, especially when studying relationships that involve both short-term dynamics and long-term equilibrium and also provides researchers with robust estimates and insights into the dynamics of economic phenomena over time [72]. Mahmood, Mann and Zwass [73]; and Kao and Chiang [74] extended the application of the DOLS estimator to panel datasets and found that it performs better than both the OLS estimator and the FMOLS estimator, regardless of whether the panel is homogeneous or heterogeneous. They observed that the FMOLS estimator does not significantly enhance prediction compared to the OLS estimator.

Beenstock and Felsenstein [75] pointed out that the failure of the nonparametric correction for FMOLS can have serious consequences. Variables were measured in kilo tons for CO2 emissions, Rs. 10 million for real GDP, and square kilometer (sq. km.) for the forested area. Time series data from 1990 to 2020 for Nepal was used based on the World Development Indicator (WDI) and Nepal Rastra Bank dataset (see Table 2). Since Grossman and Krueger [43] contend that there is a nonmonotonic relationship between economic growth and CO2 emissions, GDP is considered in this study’s theoretical framework. For CO2 emissions, forests perform a crucial dual role. Carbon dioxide from the atmosphere is absorbed and stored by forests and their tree biomass, a process known as carbon sequestration, whereas CO2 from deforestation and tree cutting is released into the atmosphere.

Table 2. Data used.

Year	GDP (Rs. 10 million)	CO2 emissions (kt)	Forest area (sq. km)	LNGDP	LNCO2emmi	LN forest area
1990/91	12,037.00	938.80	56,720.00	9.395,741	6.844,602	10.94588
1991/92	14,948.70	1182.25	56,828.76	9.61238	7.075175	10.9478
1992/93	17,147.40	1234.16	56,937.52	9.749,602	7.118,146	10.94971
1993/94	19,927.20	1409.80	57,046.28	9.899,841	7.251,203	10.95162
1994/95	21,917.50	1744.50	57,155.04	9.995,041	7.464,223	10.95352
1995/96	24,891.30	1895.70	57,263.80	10.12227	7.547,343	10.95542
1996/97	28,051.30	1959.10	57,372.56	10.24179	7.58024	10.95732
1997/98	30,084.50	2191.37	57,481.32	10.31177	7.692,282	10.95922
1998/99	34,203.60	2315.70	57,590.08	10.44009	7.747,467	10.96111
1999/00	37,948.80	3123.70	57,698.84	10.54399	8.046773	10.96299
2000/01	44,151.90	3221.00	57,807.60	10.69539	8.077447	10.96488
2001/02	45,944.26	3464.60	57,988.87	10.73518	8.150,352	10.96801
2002/03	49,223.08	2786.70	58,170.14	10.80412	7.932,613	10.97113
2003/04	53,674.91	3021.80	58,351.41	10.8907	8.013608	10.97424
2004/05	58,941.17	2809.60	58,532.68	10.9843	7.940,797	10.97734
2005/06	65,408.41	3188.60	58,713.95	11.08841	8.067337	10.98043
2006/07	72,782.70	2616.10	58,895.22	11.19523	7.86944	10.98352
2007/08	81,565.82	2693.60	59,076.49	11.30917	7.898,634	10.98659
2008/09	98,827.15	2994.90	59,257.76	11.50113	8.004666	10.98965
2009/10	119,277.36	3882.80	59,439.03	11.68921	8.264,312	10.99271
2010/11	156,268.10	4640.90	59,620.30	11.95933	8.442,664	10.99575
2011/12	175,837.92	5199.20	59,620.30	12.07732	8.55626	10.99575
2012/13	194,929.48	5997.80	59,620.30	12.18039	8.699,148	10.99575
2013/14	223,252.53	6087.80	59,620.30	12.31606	8.714,042	10.99575
2014/15	242,363.85	7132.20	59,620.30	12.3982	8.872,375	10.99575
2015/16	260,818.44	7186.20	59,620.30	12.47158	8.879,918	10.99575
2016/17	307,714.49	10,735.70	59,620.30	12.63693	9.28133	10.99575
2017/18	345,595.00	13,265.30	59,620.30	12.75302	9.492,907	10.99575
2018/19	385,893.00	15,139.40	59,620.30	12.86332	9.625,056	10.99575
2019/20	388,870.00	13,860.50	59,620.30	12.871	9.536,798	10.99575
2020/21	435,260.00	14,949.20	59,620.30	12.9837	9.612,413	10.99575

This study employs econometric analysis to try to establish a causal link between deforestation and the CO2 emissions that South Asian nations contribute to. So, one of the repressors’ in this study is an annual forested area (F.A.) [76]. The normal asymptotic distribution of the DOLS estimators and their standard deviations offer a reliable test for the variables’ statistical significance [23]. By estimating the dependent variable (CO2 emissions, in kt.), on explanatory variables annual forested area, in sq. km., and GDP(Rs. 10 million) in 10 million Nepalese Rupees, in levels, leads, and lags, the DOLS approach is effective in the occurrence of a mix order of integration, enabling integration of the individual variables in the cointegrated outline. According to Grossman and Krueger [43]; the following model can take into account the non-linear relationship between GDP and carbon emissions:

(1)

Due to the accumulation of leads and lags among the explanatory variables, this estimator consequently gives solutions to the problems of small sample bias, endogeneity and autocorrelation [11]. This study finally estimates the following equation:

(2)

The long-run elasticities of the explanatory variables, such as carbon emissions, GDP and forested area, are indicated by the coefficients (β₁, β₂, and β₃), respectively. The DOLS method was used to find the different levels of integrations of dependent and independent variables, and it is also used when there is endogeneity in independent variables.

4. Results and Discussion

4.1. Results

This section mainly covers descriptive statistical results of the DOLS model to find the relationship between the short-run and long-run nature of CO2 emission and GDP, including forest covered areas. It also checks the robustness of the model.

This research presents statistical metrics related to three variables: CO2 emissions in kilotons, forest area in square kilometers and GDP in 10 million rupees. These metrics provide insights into the distribution and characteristics of these variables within the dataset comprising 31 observations (see Table 3).

Table 3. Descriptive statistic.

	CO2 emissions kt.	Forest area sq. km.	GDP Rs. 10 million
Mean	4931.257	58,520.99	130,572.8
Median	3123.700	58,713.95	65,408.41
Maximum	15,139.40	59,620.30	435,260.0
Minimum	938.8000	56,720.00	12,037.00
Std. Dev.	4234.631	1069.063	129,698.1
Skewness	1.413,206	−0.303,536	1.039611
Kurtosis	3.708,163	1.512,931	2.745,763
Jarque–Bera	10.96637	3.332,383	5.667,572
Probability	0.004156	0.188,965	0.058790
Sum	152,869.0	1,814,151.	4,047,757.
Sum sq. Dev.	5.38E + 08	34,286,893	5.05E + 11
Observations	31	31	31

4.2. Trend Lines of lnCO2 and lnGDP

The trend line of the natural logarithm of CO2 emission is observed as linear, with some fluctuation observed on the trend of the natural logarithm of GDP, but it also has a linear trend over time (see Figure 1).

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

Trend line of natural logarithm of CO2 and GDP.

5. Trend Line of Forested Area in Nepal

The trend line of forest area shows that it has continuously increased from the fiscal year 1990/91 to 2010/11, which is then constant (see Figure 2).

5.1. Unit Root Tests

The unit root test is usually used to evaluate data stationarity. By looking for the presence of a unit root, the Augmented Dickey–Fuller (ADF) test is used to determine if variables meet the requirements for stationarity [77, 78].

The outcomes of the Augmented Dickey–Fuller unit root test for the three variables natural logarithm of CO2 emissions (LNCO2), natural logarithm of GDP (LNGDP) and LNFA are shown in Table 4. The test displays the p value along with statistical statistics for several scenarios. LNFA requires two differences, but LNCO2 and LNGDP probably have trends that eventually become stationary after one difference. Therefore, the application of the DOLS analysis is also validated by the presence of mixed-order integration for variables estimated by the ADF.

Table 4. Unit root test (ADF: with constant and trend).

Series	On level		On first difference		On second difference
Series	t-stat	p value	t-stat	p value	t-stat	p value
LNCO2	−1.5195	0.8000	−3.4967	0.0599 ^∗∗∗
LNGDP	−1.9411	0.6076	−4.1616	0.0140 ^∗∗
LNFA	−1.3972	0.8403	−1.6585	0.7436	−5.335,024	0.0000 ^∗

Note: ^∗, ^∗∗ and ^∗∗∗ denote significance at the 1%, 5% and 10% levels, respectively.

5.2. Selection Criteria for the Lag Order in VAR

Determining the proper lag time is crucial before doing a cointegration test. The numerous criteria in the table advise the use of a lag length of 2 for follow-up tests (see Table 5).

Table 5. VAR lag order Selection criteria.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	122.2459	NA	4.01e − 08	−8.517,567	−8.374,831	−8.473,931
1	243.9415	208.6209	1.29e − 11	−16.56725	−15.99630 ^∗	−16.39271
2	258.3775	21.65398 ^∗	8.97e − 12 ^∗	−16.95553 ^∗	−15.95638	−16.65008 ^∗

^∗The optimal lag order is typically chosen as the one with the smallest value of the selected criterion.

5.3. Cointegration

The cointegrated variables are related to more than one cointegrating relationship. The Johansen test gives test statistics for the total number of cointegrating equations and estimates for all such cointegrating equations. The following table shows the result of the Johansen cointegration test.

The trace and max-eigen test results, as well as the results of the unconstrained cointegration rank tests, are shown in Table 6. The number of cointegrating equations (C.E.s) in the system is ascertained using these tests. The hypothesis “None” for the trace test indicates that there are no C.E.s, and it is rejected with a very low probability (0.0008), suggesting that cointegration is present. Similarly, the max-eigen test’s rejection of the hypothesis “None” with a probability of 0.0050 likewise lends credence to the system’s cointegration.

Table 6. Cointegration test: Johansen.

Hypothesised	Eigenvalue	Trace	Prob.	Max-eigen	Prob.
No. of CE(s)	Eigenvalue	Statistic	Prob.	Statistic	Prob.
None	0.642,331	43.29142	0.0008	27.76000	0.0050
At most 1	0.395,702	15.53142	0.0494	13.59957	0.0635
At most 2	0.069050	1.931,849	0.1646	1.931,849	0.1646

The provided output represents the results of a Dynamic Ordinary Least Squares (DOLS) regression analysis. This analysis aims to model the relationship involving the dependent variable LNCO2 and the independent variables LNGDP and D_LNFA (first difference of natural logarithm of forest area) (see Table 7).

Table 7. Results of DOLS.

Variable	Coefficient	Std. error	t-statistic	Prob.
LNGDP	0.611,163	0.035495	17.21848	0.0000
D_LNFA_	−68.36836	28.81329	−2.372,806	0.0352
C	1.598,451	0.472,133	3.385,598	0.0054
R-squared	0.985,435	Mean dependent var		8.274,449
Adjusted R-squared	0.970,870	SD dependent var		0.599,828
SE of regression	0.102,376	Sum squared resid		0.125,769
Long-run variance	0.008865

5.4. Natural Logarithm of GDP

This indicates that a 1% increase in the GDP is associated with an approximate 0.6112% increase in the LNCO2, assuming all other factors remain constant. The probability associated with this t-statistic is very close to 0, indicating a high significance level.

5.5. Natural Logarithm of Forest Area

This suggests that a 1% increase in the forest area is associated with an approximate 68.39% decrease in the LNCO2, assuming all other factors remain constant. The probability is 5%, indicating vital significance. Similarly, the results further show that the empirical analysis demonstrates that a 1% augmentation in forest area yields a considerable reduction, approximately 68.39%, in the LNCO2, holding all other variables constant. This finding carries substantial statistical significance, as indicated by the probability level of 5%.

5.6. Constant (Intercept)

The constant term 1.5985 still represents the estimated value of the LNCO2 when both predictor variables (LNGDP and LNFA) are zero. The t-statistic is 3.38, and the probability is once closer to 0, suggesting high significance.

5.7. Model Goodness of Fit and Long-Run Variance

The analysis provides the coefficient of determination (R-squared) as 0.9854, indicating that the model explains a substantial portion of the variation in the dependent variable. The adjusted R-squared, which accounts for the number of independent variables, is 0.9708. The standard error of the regression (S.E. of regression) is 0.102,376, and the sum squared residuals is 0.125,759. The long-run variance estimate is 0.008865, indicating the variability in the dependent variable over the long term.

5.8. Normality Test

The Jarque–Bera test is used to determine whether the distribution of the model’s variables meets the requirement for normality. This test’s significance indicates that the variables are distributed normally (see Figure 3).

The result of the Jarque–Bera test indicates that the null hypothesis is accepted because the test’s probability is more significant than a 5% level of significance. Since the probability value of Jarque–Bera (0.793,933) is greater than 5%, the model’s residual follows the normal distribution (see Figure 3).

Table 8 presents the results of a cointegration test focusing on Hansen Parameter Instability for the given series. The analysis suggests that the series are likely cointegrated, as the p value is more significant than 0.2, indicating that the null hypothesis of cointegration cannot be rejected at this level of significance.

Table 8. Cointegration test: Hansen parameter instability.

Lc statistic	Stochastic	Deterministic	Excluded	Prob. ^∗
Lc statistic	Trends (m)	Trends (k)	Trends (p2)	Prob. ^∗
0.069456	2	0	0	> 0.2

Note: ^∗Hansen [79] Lc (m2 = 2, k = 0) p values, where m2 = m-p2 is the number of stochastic trends in the asymptotic distribution.

The Wald Test examines the joint significance of the coefficients in the regression equation. With a very low probability (p < 0.0001) for both the F-statistic and chi-square, we reject the null hypothesis that all coefficients are equal to zero, indicating that at least one of the coefficients is significant. Specifically, the coefficients for LNGDP, D_LNFA, and the constant are individually significant, suggesting they contribute meaningfully to the model (see Table 9).

Table 9. Wald test.

Test statistic	Value	df	Probability
F-statistic	1563.701	(3, 12)	0.0000
Chi-square	4691.104	3	0.0000
Null hypothesis: C(1) = 0, C(2) = 0, C(3) = 0

Normalized Restriction (=0)		Value	Std. Err.

C(1) = LNGDP		0.611,163	0.035495
C(2) = D_LNFA		−68.36836	28.81329
C(3) = constant		1.598,451	0.472,133

Note: Restrictions are linear in coefficients.

6. Discussion

The results show that a 1% increase in the GDP is associated with an approximate 0.6112% increase in the LNCO2, assuming all other factors remain constant. The result of this study is supported by the study of Begum, Raihan and Raihan [76]; who found the coefficient of economic growth is positive and significant with CO2 emissions, meaning that an RM1 million increase in the GDP is associated with an increase in CO2 emissions of 0.931-kilo tons. Instead, the long-run coefficient of the forested area was negative and significant, implying that declining 1 hectare of forested area (i.e., deforestation) impacts three-kilo tons of CO2 emissions in Malaysia.

Similarly, the results of this study are supported by the study of Hao et al. [65] and Nathaniel and Khan [67]; who found that diverse facets of green growth, encompassing linear and nonlinear elements, lead to a decline in CO2 emissions. Additionally, implementing environmental taxes, enhancing human capital, and adopting renewable energy sources contribute significantly to this decrease. Conversely, the investigation underscores that economic growth, trade activities, and the utilization of nonrenewable energy sources intensify environmental degradation across ASEAN nations, highlighting the environmental toll associated with economic progress and the impact of urbanization on energy consumption patterns.

But Ahmed et al. [63]; Shan et al. [64]; Tongwane and Moeletsi [66]; Zhou, Tang, and Zhang [68]; Akram et al. [69]; Rahman [70] and Işık and Çelik [61] partially supported this study’s results who analyze the shared characteristics and causal relationships among hypotheses and contributes insights into the dynamics between the environment and the economy. It suggests a threshold expenditure level of approximately 15% of real GDP per capita to effectively balance economic growth with environmental preservation across seven U.S. states. Additionally, the study highlights that economic growth, trade activities, and the utilization of nonrenewable energy sources exacerbate environmental degradation in Asian countries, underscoring the environmental consequences associated with economic development and emphasizing urbanization’s influence on energy consumption patterns.

7. Conclusion

This study examined the dynamic impacts of economic growth and forested area on CO2 emissions in Nepal, utilising a DOLS approach. The analysis covered data from 1990 to 2020, focusing on CO2 emissions (measured in kilotons), real GDP (measured in Rs. 10 million), and forested area (measured in square kilometers). The analysis revealed a significant positive relationship between Nepal’s GDP and CO2 emissions. Specifically, a 1% increase in GDP was associated with a 0.61112% increase in CO2 emissions, which suggests that economic growth fosters carbon emissions at an accelerated rate in the long run.

Conversely, a significant negative relationship existed between forested areas and CO2 emissions. A 1% increase in forested areas led to a substantial 68.37% decrease in CO2 emissions. This underscores the crucial role of the forested regions in mitigating carbon emissions through carbon sequestration and highlights the adverse impact of deforestation on emissions in Nepal.

These findings suggest that effective policy measures and economic instruments, such as afforestation, reforestation, forest conservation and sustainable forest management, can be instrumental in reducing carbon emissions in Nepal while maintaining long-term economic growth. This research contributes valuable insights into the complex dynamics between economic growth, forested areas, and CO2 emissions in Nepal, emphasising the importance of balancing economic development with environmental conservation efforts to achieve sustainable development and mitigate climate change.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This study did not receive any funding. The authors managed all the costs for preparing the manuscript. However, the APC charge has been covered by the waiver policy of Hindawi Journals.

Open Research

Data Availability Statement

This research utilized the secondary dataset collected from the official website of Nepal Rastra Bank and World Bank. The official website of Nepal Rastra Bank is https://www.nrb.org.np/category/annual-reports/?department%3Dred, and the official website of the World Bank is https://data.worldbank.org/country/nepal from where we can obtain the data used in this study.

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Balancing Progress and Preservation: The Complex Interplay of Economic Growth and Forest Conservation in Nepal’s Carbon Dioxide Emissions

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