Forest Resources Research Articles

3D Modeling Simulation and Innovative Design of a Saw Cutting Mechanism for a Movable Buckling Machine

In this research, a sawing mechanism of a mobile buckling machine was innovatively designed for the forest resources and environmental characteristics of the northeast region of China. The northeast region of China is rich in forest resources, but the climate is cold, which puts high demands on the performance and adaptability of mechanical equipment. In this study, the three-dimensional modeling of the saw-cutting mechanism was completed by Pro/Engineer 5.0 modeling software, and an in-depth dynamics simulation analysis was carried out using dynamics simulation software. The study aims to assess the applicability of this saw-cutting mechanism in the northeast region of China, analyze its design characteristics, and explore possible directions for performance optimization. Through this study, we expect to provide valuable references for future technological innovations, as well as to promote the practical application and development of mobile buckling machines in cold regions. This research not only has regional characteristics, but also reflects the innovative idea of combining mechanical equipment design with environmental adaptability.

From resources to capital: Investigating the efficiency of forest ecosystem products value realization in China

Forest resources provide a good ecological environment for human beings and offer economic well-being and benefits. Value realization of ecosystem products reflects the process from resources to capital. This study investigates the two-stage value realization efficiency of China's forest ecosystem products from 2011 to 2021. A parallel nested network Data Envelopment Analysis model is applied to assess product supply and value transformation stages. In addition, the evolution of the spatial pattern is depicted by a multilayer Standard Deviational Ellipse. The results reveal that (1) the overall value realization trend of China's forest ecosystem products shows a fluctuating growth trend, with an average annual efficiency value of 0.86. (2) The efficiency level of the value transformation stage is lower than that of the product supply stage. (3) Regional disparities persist. The efficiency values in East China and South China are higher than that in other regions. (4) The value realization capacity of China's forest ecosystem products may be closely related to national development strategies and policy orientations.

The Faxinal System in the State of Paraná: a Review

The Faxinal System is a traditional silvopastoral system located in fragments of Araucaria Forest in the state of Paraná (PR). In these areas, there is extensive animal husbandry between properties associated with the selective exploitation of forest resources. The Faxinal has been the subject of study by various branches of science, so new gaps and methods have emerged as research has progressed on the subject. To verify the evolution and new challenges of research on faxinals, a bibliometric review was carried out based on 125 articles collected by the Publish or Perish 8 program. The articles were organized according to the decade of publication (1980s; 1990s; 2000s; 2010s and early 2020s), the topic researched (economy, territory, cultural and environmental)and the geographical location of the faxinals. The number of articles on the Faxinal System has increased in recent decades, with a tendency for an even greater increase by 2029. Research has shown that, due to capitalist expansion,the strengthening of agribusiness and environmental problems, the Faxinal is failing to sustain itself in the traditional way and its long-term continuity is under threat. Considering this, we see the need for an integrated approach that promotes cooperation between the community, the private sector and government entities. In addition, initiatives aimed at research, education, training, technical support, and monitoring are fundamental to maintaining the Faxinal System, taking advantage of its sustainable potential.

Trends of forest cover and prospects of Great Ethiopian Renaissance Dam (GERD) to Ethiopia and the downstream countries

The need for forest products, agricultural expansion, and dependency on biomass for the household energy source has largely influenced Ethiopia’s forest resources. Consequently, the country lost its forest resources to less than 6% until the millennium. In this study, quantitative and qualitative historical data analysis was employed to understand the socioeconomic benefits of large dam construction to Ethiopia and downstream countries. Moreover, remotely sensed data was also used to analyze the trends of vegetation cover change in the Nile catchment since the commencement of the dam; focusing on areas where there are high settlement and urban areas. It was identified that Ethiopia has one of the lowest electricity consumption per capita in Africa; about 91% of the source of household energy supply depends on fuelwood today and more than 55.7% of the population does not have access to electricity. The normalized difference vegetation index result shows an increment of vegetation area in the Nile catchment and a reduction of no vegetation area from 2011–2021 by 37.1%; which is directly related to the protection of the dam catchment for its sustainability in the last decade. The hydroelectric dam construction has prospects of multi-benefit to Ethiopia and downstream countries either through the direct benefit of hydropower energy production, related socioeconomic values, and reducing risks of destructive flood from Ethiopian highlands. Generally, it explains the reason why to not say ‘No’ to the reservoir as it is an ever more vital tool for fulfilling growing energy demand and supporting ecological stability.

The impact of war on forest logging: Changes in logging practices in Syrian rural communities

Syria has been struggling with a prolonged and brutal war for over a decade, leaving much of the country in devastation and its forests severely degraded. While many studies have identified logging for firewood as a significant driver of deforestation during the war, there is a notable absence of research specifically addressing logging behavior within the context of war. This research seeks to address this gap by examining the changes in logging behavior among local communities, the effects of violence and socioeconomic variables on wood harvesting, the contribution of wood income to people's survival during the war. To do that, we conducted survey questionnaires in two coastal villages in Syria—Blouta and Nehel al Annaze—which experienced high and low levels of violence, respectively. Our findings reveal that logging activities increased once the war began, with more people involved in forest harvesting and average days spent logging per week significantly increasing from 1.5 to 4. Additionally, the war appeared to reduce the gender disparity in logging activities: before the conflict, women engaged in logging significantly more frequently than men, but this difference became insignificant during the war. The level of violence alone did not significantly affect the amount of wood logged per month. Furthermore, income generated from wood harvesting played a substantial role in bridging the gap between the monthly income needed to cover their basic necessities and the actual income of families in both villages by 46.94 % and 84.83 % of the gap in Nehel al Annaze and Blouta, respectively. These results highlight the crucial role of forest resources in supporting rural livelihoods during times of war. Post-war effective forest management will be vital to ensure the sustainable use and recovery of forest resources, supporting both ecological restoration and economic stability for the rural communities.

Deep learning with ensemble approach for early pile fire detection using aerial images

Wildfires rank among the world’s most devastating and expensive natural disasters, destroying vast forest resources and endangering lives. Traditional firefighting methods, reliant on ground crew inspections, have notable limitations and pose significant risks to firefighters. Consequently, drone-based aerial imaging technologies have emerged as a highly sought-after solution for combating wildfires. Recently, there has been growing research interest in autonomous wildfire detection using drone-captured images and deep-learning algorithms. This paper introduces a novel deep-learning-based method, distinct in its integration of infrared thermal, white, and night vision imaging to enhance early pile fire detection, thereby addressing the limitations of existing methods. The study evaluates the performance of machine learning algorithms such as random forest (RF) and support vector machines (SVM), alongside pre-trained deep learning models including AlexNet, Inception ResNetV2, InceptionV3, VGG16, and ResNet50V2 on thermal-hot, green-hot, and white-green-hot color images. The proposed approach, particularly the ensemble of ResNet50V2 and InceptionV3 models, achieved over 97% accuracy and over 99% precision in early pile fire detection on the FLAME dataset. Among the tested models, ResNet50V2 excelled with the thermal-fusion palette, InceptionV3 with the white-hot and green-hot fusion palettes, and VGG16 with a voting classifier on the normal spectrum palette dataset. Future work aims to enhance the detection and localization of pile fires to aid firefighters in rescue operations.

The effects of disturbance on woody species composition, structure and regeneration status in the ura natural forest, Guangua district, Awi Zone, Ethiopia

Ethiopia Province has diverse forest resources. However, it has decreased over time, leaving the country's forest with only remnant patches. Therefore, the present study was conducted to examine the species composition, structure and regeneration status of woody plant species along disturbance gradients. To collect the data, the forests were first stratified into low disturbed (LD), moderately disturbed (MD) and heavily disturbed (HD) groups based on information from reconnaissance site visits and consultations with key informants. Three transect lines were laid systematically 200 m apart, and rectangular plots 10 m∗20 m (200 m2) in length were laid at 50 m intervals along each transect, for a total of 27 plots. The DBH and height of all woody individuals with ≥5 cm and ≥2 m were measured. Seedling and sapling data were recorded in four subplots of 2 m∗5 m (10 m2) respectevly in the opposite corner of the main plot. Woody species density, richness, evenness, diversity, frequency and IVI were determined for each disturbance class, and one-way ANOVA and mean comparison tests were used to examine significant differences among disturbance classes. A total of 37 woody species and 27 families were recorded. Based on structural analysis, only Croton macrostachyus exhibited an inverted J shape. It is concluded that woody species composition was affected by human disturbance. Therefore, a concentrated form of forest conservation should be established to protect against excessive human disturbance.

Emergence of Forest Environmental Kuznets Curve in Africa: Some New Evidences

ABSTRACT: The African continent, renowned for its diverse landscapes and abundant biodiversity, grapples with a critical environmental challenge – deforestation. The swift depletion of forests, serving as the primary habitat for 80% of the world’s terrestrial animals and plants, poses a severe threat in Africa. Human activities, including logging, agricultural expansion, and urbanization, have jeopardized numerous ecosystems, exacerbating the overarching global climate crisis. Beyond serving as mere collections of trees, forests are intricate ecosystems supporting a staggering diversity of flora and fauna. The repercussions of forest loss in Africa are profound, resulting in a significant decline in biodiversity. Numerous species, some exclusive to these ecosystems and found nowhere else on the planet, now teeter on the brink of extinction. The predicament of deforestation in Africa is intimately intertwined with the broader global challenge of climate change. Urgency is paramount in addressing this crisis, as the loss of forests impacts not only local ecosystems but also exacerbates the overarching climate emergency. Reforestation emerges as a pivotal solution, offering an opportunity to reverse the damage inflicted on these vital ecosystems. This study examines whether forest natural resources can explain the environmental Kuznets curve hypothesis in African countries. The empirical strategy is based on the second-generation cointegration approach, cross-sectional dependency autoregressive distributed lag (CS-ARDL) and Augmented Mean Group (AMG) estimator. The findings show a potential long-run relationship between the forest rent and the environment. Further analysis highlights the importance of effective management of natural forest resources for improving environmental quality in Africa. We recommend that policymakers prioritize the restoration of ecosystems that have suffered carbon depletion due to human activities. Restoration efforts should strategically aim to enhance carbon sequestration capacity and rebuild biodiversity. Additionally, the establishment of robust monitoring and reporting systems is imperative to track current carbon emissions from forests. This entails regular assessments of forest carbon stocks, land-use changes, and the effectiveness of ongoing conservation and restoration initiatives.

공공문화콘텐츠로서의 산림교육과 자연해설에 대한 해석적 연구: 프리먼 틸든의 ‘해설’ 개념을 중심으로

This study is an interpretive research on the philosophical orientation of forest education, a public cultural content utilizing forest resources. Forest education professionals such as ‘forest interpreters’, ‘early childhood forest guides’, and ‘forest trail and mountain guides’ are trained to provide public cultural content services that mediate between citizens and nature. This practice originated in the historical context of nature preservation and management through state-led national parks, which began in the West, particularly in the United States. Forest education in Korea, such as forest interpretation, is equivalent to ‘nature interpretation’ in the West. The various literatures produced on nature interpretation have had a significant impact on Korean forest education policy, and this study aims to identify the ideological roots of nature interpretation, which have been largely unexplored. The study first approached nature interpretation activities by collecting examples from Europe, Northern Europe, and Canada, confirming that nature preservation is a common goal and purpose. Through a historical approach, the researchers examined the preservationism that influenced the introduction of nature interpretation in the West and realized that the philosophical origin of nature interpretation is the idea of combining nature preservation and spirituality. To understand nature interpretation, we examine Freeman Tilden's concept of interpretation, which continued this early preservationist spirit but constructively transformed it and shaped nature interpretation today. The study explored the core concept of ‘the Thing Itself’ in the context of Romantic texts such as those by Novalis and Carl von Linné, centering on Kant's influence on Romanticism. Most importantly, the researchers interpreted Tilden's concept of ‘interpretation’ as a theory of the sublime to explore the philosophical roots of nature interpretation. Through this study, it is argued that the essential meaning of the field of nature interpretation is the unity of nature and human beings and the love of nature through personal experience and recognition of the sublime in nature.

The Yaya Reserve: From the Evolution of its Plant Cover to Proposals for Sustainable Management

The Yaya reserve, one of the biodiverse forests in Ivory Coast, faces significant threats from agricultural colonization, illegal logging, and demographic pressure, which could jeopardize its sustainability. This paper focuses on contributing to the reserve’s sustainable management. To achieve this, three categories of data were collected: remote sensing data, floristic inventory data, and survey data from the neighboring populations. Remote sensing data were obtained by downloading images from the USGS earth explorer website. Floristic data were obtained through surface and itinerant survey. Survey data were collected using a structured interview. The images were processed to calculate the areas of various land use classes. The floristic data were analyzed for species richness and composition, while the survey data were analyzed to understand local perceptions of landscape dynamics, current management practices, and proposals for participatory management. The results revealed two major periods in the forest’s dynamics: increased deforestation from 1986 to 2005 and subsequent restoration from 2005 to 2022. These phases of degradation and restoration are likely related to recent social and political crises and management challenges. The floristic inventory identified 167 woody species across 134 genera and 47 families. 24 species (14% of the total) are endemic to West Africa, and 21 species (12.42%) are listed on the IUCN red list, indicating a relatively good conservation of the forest. The survey data highlighted the vulnerability of the forest and underscored the importance of centralized decision-making in management. The study suggests that participatory management could be a viable alternative for protecting the reserve. The hypotheses clearly states that the active involvement of local populations in the sustainable exploitation of forest resources can improve living standards, while helping to preserve the forest from exploitative practices.

Relationship between economic development, forest resources, and forest fires: European context

Conservation of forest resources is a prerequisite for sustainable development of human society, both in the context of preventing negative climate change and for economic growth. The study aims to establish or refute the co-dependence between the level of forest cover in European countries and the production of gross domestic product. The study object is the socio-economic systems of the national economies of European countries in relation to the totality of forest resources of the continent. Studying the dynamics of forest cover indicators (the share of forests in the total area of the country and forest area per capita), weighted within the internationally recognized regions of Europe, it is confirmed that the level of forest cover of European countries is gradually increasing. The analysis of forest fire area maps identifies three main groups by the level of vulnerability to forest fires: safe (Northern European countries), conditionally safe (Western European countries), and dangerous (Eastern and Southern European countries).Denmark, Finland, France, Norway, and Finland show a direct correlation between the level of forest cover of a country’s territory and gross domestic product. The results of cluster analyses based on the data from 2000, 2010, 2015, and 2020 confirm the existence of a stable cluster of European countries (34 countries) in which there is one type of relationship between the production of gross domestic product and the level of forest cover of the territory. AcknowledgmentThis study was funded by the grant support from the Philipp Schwarz Initiative of the Alexander von Humboldt Foundation.

Determinants of Dependency Between Local Communities and Forest Resources Around a Protected Area in Cameroon

Determinants of Dependency Between Local Communities and Forest Resources Around a Protected Area in Cameroon

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning.

The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province. Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019). The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019. This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Significance of Model Forest Stakeholders in the Management of Sustainable Forest Resources: The Case of Yalova Model Forest, Turkey

Significance of Model Forest Stakeholders in the Management of Sustainable Forest Resources: The Case of Yalova Model Forest, Turkey

The forest-water-nexus: A narrative construction of the (ir-)relevance of cooperation

Interlinkages between forest and water resources are changing with global warming, leading to an increased likelihood of severe disturbances such as floods and droughts. In order to develop responses to these changing interlinkages and their effects on local communities and ecosystems, scholars propose to increase interactions across the forest and water sectors. In Germany, however, the two respective sectors have been observed as institutionally and ideationally fragmented, showing very few signs of cross-sectoral cooperation. As local actors are most directly affected by global warming and struggle to adapt to its consequences, we argue that cross-sectoral cooperation is also relevant at the local scale. Against this background, it is our aim to investigate the local narrative constructions of the (ir)relevance of cooperation across the forest and water sectors. Based on semi-structured interviews and focus group discussions in six case study areas, we identify eight narratives which range from irrelevance (4), via partial relevance (2), to relevance of cooperation (2). Additionally, we elucidate a disinterest in cooperation among local water actors, compared to a narrative of urgency and fatalism among forest actors. Disinterest and urgency, however, furthermore differ regionally: Disinterest in cooperation is predominant in water abundant regions, whereas urgency is predominantly observed in regions of water scarcity. We contribute to conceptualizing disinterest as the rejection of a (common) problem possibly related to defend one's turf, and related both the urgency and fatalism narratives to the high affectedness within the forest sector.

The impact of changes in Russian log export tariffs on China's log imports

Russia is a major log supplier to China, and in order to control log exports, Russia has adjusted its log export tariffs. This paper analyzes the impact of changes in Russian log export tariffs on China's log imports using a gravity model based on China-Russia log trade data from 2003 to 2022. The conclusions show that the difference in forest resource endowment between the two countries promotes China's log imports, while the distance between China and Russia and Russia's log export tariffs harm China's log imports, and the effect of export tariffs is significant. The further research shows that the suppression effect of the changes in Russian log tariffs on China's log imports is very significant in the short term, but relatively weak in the long term. Based on the above conclusions, this paper recommends some policies from both short-term and long-term perspectives.

Land Cover Classification of Remote Sensing Imagery with Hybrid Two-Layer Attention Network Architecture

In remote sensing image processing, when categorizing images from multiple remote sensing data sources, the deepening of the network hierarchy is prone to the problems of feature dispersion, as well as the loss of semantic information. In order to solve this problem, this paper proposes to integrate a parallel network architecture HDAM-Net algorithm with a hybrid dual attention mechanism Hybrid dual attention mechanism for forest land cover change. Firstly, we propose a fusion MCA + SAM (MS) attention mechanism to improve VIT network, which can capture the correlation information between features; secondly, we propose a multilayer residual cascade convolution (MSCRC) network model using Double Cross-Attention Module (DCAM) attention mechanism, which is able to efficiently utilize the spatial dependency between multiscale encoder features: the spatial dependency between multiscale encoder features. Finally, the dual-channel parallel architecture is utilized to solve the structural differences and realize the enhancement of forestry image classification differentiation and effective monitoring of forest cover changes. In order to compare the performance of HDAM-Net, mountain urban forest types are classified based on multiple remote sensing data sources, and the performance of the model is evaluated. The experimental results show that the overall accuracy of the algorithm proposed in this paper is 99.42%, while the Transformer (ViT) is 96.92%, which indicates that the proposed classifier is able to accurately determine the cover type.The HDAM-Net model emphasizes the effectiveness in terms of accurately classifying the land, as well as the forest types by using multiple remote sensing data sources for predicting the future trend of the forest ecosystem. In addition, the land utilization rate and land cover change can clearly show the forest cover change and support the data to predict the future trend of the forest ecosystem so that the forest resource survey can effectively monitor deforestation and evaluate forest restoration projects.

SmokeFireNet: A Lightweight Network for Joint Detection of Forest Fire and Smoke

In recent years, forest fires have been occurring frequently around the globe, affected by extreme weather and dry climate, causing serious economic losses and environmental pollution. In this context, timely detection of forest fire smoke is crucial for realizing real-time early warning of fires. However, fire and smoke from forest fires can spread to cover large areas and may affect distant areas. In this paper, a lightweight joint forest fire and smoke detection network, SmokeFireNet, is proposed, which employs ShuffleNetV2 as the backbone for efficient feature extraction, effectively addressing the computational efficiency challenges of traditional methods. To integrate multi-scale information and enhance the semantic feature extraction capability, a feature pyramid network (FPN) and path aggregation network (PAN) are introduced in this paper. In addition, the FPN network is optimized by a lightweight DySample upsampling operator. The model also incorporates efficient channel attention (ECA), which can pay more attention to the detection of forest fires and smoke regions while suppressing irrelevant features. Finally, by embedding the receptive field block (RFB), the model further improves its ability to understand contextual information and capture detailed features of fire and smoke, thus improving the overall detection accuracy. The experimental results show that SmokeFireNet is better than other mainstream target detection algorithms in terms of average APall of 86.2%, FPS of 114, and GFLOPs of 8.4, and provides effective technical support for forest fire prevention work in terms of average precision, frame rate, and computational complexity. In the future, the SmokeFireNet model is expected to play a greater role in the field of forest fire prevention and make a greater contribution to the protection of forest resources and the ecological environment.

Effect of Microbial Degradation Treatments on Lignocellulose, Cellulose, and Water-Holding Capacity of Four Typical Forest Fuels from Northeast China

Since forest fuel decomposes slowly and increases the risk of forest fires by accumulating over the years, forest fuel management to accelerate the decomposition process is essential to prevent forest fires and protect forest resources. In this study, we conducted experiments on forest fuels (Pinus sylvestris var. mongholica, Larix gmelinii, Quercus mongolica and Fraxinus mandshurica) in four typical plantation forests in northeast China by adding Trichoderma spp. to investigate the decomposition process and the changes in cellulose, hemicellulose and the water-holding capacity of the fuels. The addition of Trichoderma spp. accelerated the decomposition of cellulose, hemicellulose and lignin in the fuel. Trichoderma spp. promoted the ratio of water absorption and loss, as well as the water-holding capacity of the fuels. The ratio of water absorption and loss reached equilibrium when the decomposition time was up to 35 days, and the addition of Trichoderma spp. increased the maximal water-holding capacity of the fuel. The residual ratio of the four types of fuel degraded by the different treatments was inversely proportional to their maximal water-holding capacities and to the residual ratios of cellulose, hemicellulose and lignin. The residual ratios of degradation of the four fuels under different treatments were linearly related to their maximum water-holding capacity, cellulose, hemicellulose and lignin residual ratios. Trichoderma spp. had a positive effect on the degradation effect and water-holding capacity of fuel on the ground surface of four typical plantation forests. The study is of positive significance for the decomposition of fuel in forests, it promotes the development of biological fire prevention technology and provides a basis for the reinforcement of the management of fuel in forests and the protection of forest resources.

Multifaceted natural resources and green energy transformation for sustainable industrial development

Climate change is the most phenomenal challenge to humanity, and its roots are intervened with unsustainable industrialization, exercising overexploitation of natural resources. Therefore, the departure from non-renewable to renewables has become inevitable, though thought-provoking. In this respect, we explore how green energy transformation moderates the impacts of multifaceted natural resources on sustainable industrial development in the presence of other covariates involving technological progress, financial development, and economic progress. We compiled data from Group of Seven (G-7) members over the 1995−2018 period and applied panel quantile regression (PQREG) to capture the effects across varying levels of quantiles of sustainable industrial development. Results revealed a positive role of natural gas rents, while coal, forest, and total natural resource rents contributed adverse implications for sustainable industrial development. However, the green energy transformation proved to be the game changer because it not only directly induced sustainable industrial development improvement but also turned the unfavorable effects of coal, forest, and total natural resources into favorable ones by interacting with those multifaceted natural resources. Technological, financial, and economic progress supported sustainable industrial development in G-7 nations, particularly in members with existing middle and upper scales of sustainable industrial development. These findings are robust enough when subjected to different estimation tools. In light of these outcomes, the interaction between green energy transformation and natural resource policy is inevitably critical to attaining natural resource efficiency for sustainable industrial development. Therefore, it is imperative to establish a close policy coordination between advancing green energy technology and allocating natural resource revenue to achieve sustainable development goals (SDGs), with a particular emphasis on SDG-7 and SDG-13.