Ecological Sectors Research Articles

The Digitalization Imperative: Unveiling the Impacts of Eco-Industry Integration on Sectoral Growth and Transformation

In the context of the global shift toward sustainable development, ecosystem-based ecological industries have become a critical area of focus for achieving green economic transformation and promoting the harmonious development of both society and the environment. This paper aims to explore the impact of industrial integration on the progression of ecological production within the framework of digitalization, with a particular emphasis on key sectors such as agriculture, forestry, and tourism. Utilizing multi-group data from 284 cities spanning the period 2011 to 2020, the study establishes a relationship between industrial integration and the growth of ecological industries, employing a two-way fixed effects model for analysis. The results demonstrate that industrial integration within ecological sectors significantly fosters sectoral development through two main mechanisms: the extension of industrial value chains and cross-sectoral synergies. Moreover, in the era of the digital economy, digitalization plays a pivotal role in facilitating the integrated development of ecological industries. The study further identifies that digital technology, digital finance, and digital expertise serve as mediating factors in this integration process. It is recommended that future initiatives focus on leveraging digital tools to enhance the integration of ecological industries, thereby advancing industrial growth. Furthermore, China is encouraged to continue promoting the integrated development of ecological industries by supporting the expansion of industrial value chains and fostering intersectoral cooperation. Simultaneously, the role of digitalization should be emphasized, with particular attention to the incorporation of relevant digital components into the industrial integration process moving forward.

Multi-objective optimal allocation of water resources in Shule River Basin of Northwest China based on climate change scenarios

The optimal allocation of water resources is crucial for addressing regional water scarcity, adapting to climate change, and promoting sustainable development. This study focused on the Shule River Basin and considered three climate scenarios (SSP1–2.6, SSP2–4.5, SSP5–8.5), which is combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). A water resources optimal allocation model including water supply and demand prediction, multi-objective optimization, decision-making and analysis on optimal water allocation schemes was constructed. This model aims to analyze the optimal allocation of water resources in the Shule River Basin over three planning years (2025, 2030, 2035), considering guarantee rates of 50 % and 75 % under various climate scenarios. The obtained results indicate that: (1) An increasing trend in water demand in the planning year. For instance, under the SSP1–2.6 scenario, the basin's water demand is projected to reach 1.920×10^9 m3, 2.037×10^9 m3, and 2.103×10^9 m3 in 2025, 2030, and 2035, respectively. (2) The total water supply with a guarantee rate of 50 % (75 %) in the planning years is projected to be 1.939 (1.483)×10^9, 1.957(1.502) ×10^9, and 1.974 (1.519) ×10^9 m3, respectively. (3) In the three scenarios of SSP1–2.6, SSP2–4.5, and SSP5–8.5, the water demand for domestic and ecology is met in each planning year. There is a slight water shortage in the industrial sector, with a shortage rate ranging from 0 % to 13.41 %. The agricultural sector experiences the highest water shortage, with a rate of 1.93–44.77 %. (4) The water supply structure of each optimal allocation scheme of water resources was optimized. The economic benefits mainly depend on the industrial and ecological sectors. These findings offer valuable insights for optimizing the distribution of regional water resources in response to changing climatic conditions.

Long-term water demand forecasting using artificial intelligence models in the Tuojiang River basin, China.

Accurate forecasts of water demand are a crucial factor in the strategic planning and judicious use of finite water resources within a region, underpinning sustainable socio-economic development. This study aims to compare the applicability of various artificial intelligence models for long-term water demand forecasting across different water use sectors. We utilized the Tuojiang River basin in Sichuan Province as our case study, comparing the performance of five artificial intelligence models: Genetic Algorithm optimized Back Propagation Neural Network (GA-BP), Extreme Learning Machine (ELM), Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Random Forest (RF). These models were employed to predict water demand in the agricultural, industrial, domestic, and ecological sectors using actual water demand data and relevant influential factors from 2005 to 2020. Model performance was evaluated based on the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), with the most effective model used for 2025 water demand projections for each sector within the study area. Our findings reveal that the GPR model demonstrated superior results in predicting water demand for the agricultural, domestic, and ecological sectors, attaining R2 values of 0.9811, 0.9338, and 0.9142 for the respective test sets. Also, the GA-BP model performed optimally in predicting industrial water demand, with an R2 of 0.8580. The identified optimal prediction model provides a useful tool for future long-term water demand forecasting, promoting sustainable water resource management.

Flash drought fades away under the effect of accumulated water deficits: the persistence and transition to conventional drought

Flash drought typically refers to the rapid intensification process that would only persist for a certain amount of time. In spite of short duration, flash drought may cause destructive impacts on agricultural and ecological sectors due to the sustained drought severity during or after the rapid intensification period. Understanding the persistence of flash drought, its regional patterns, and to what extent a transition from rapid intensification to conventional drought occurs is beneficial for drought monitoring and drought management. Employing surface and root-zone soil moisture reanalysis datasets, the notion of accumulated water deficits over varying time scales (can be detected by the moving averages of daily soil moisture series from 1 to 300 d) was introduced to explore how the signal of flash drought fades away over China during the period of 1950–2021. Results show that the flash drought signal gradually attenuates under the increased time scales of water deficits. With significant spatial differences, more than 50% of flash drought on average would be lost at a 10 d time scale, and the attenuation ratio may reach 90% when the time scale increases to 100 d. Under the effects of accumulated water deficits, the majority of flash drought events may evolve into conventional drought before dissipating completely. Soil moisture memory has a finite effect on the attenuation of flash drought signal. Flash drought signal dissipates slowly in areas with strong soil moisture memory. As time scale increases, both flash drought signal and the memory of soil moisture decrease, and their correlation also weakens.

Selection process in the Operations area of a company in the ecological sector

The present research to investigation shows different problematics in the selection to process for the operational area, and that occurs in the national and international companies so omit certain procedures that do not allow them too correctly future personal. Hence, it could be observed that the objective is to know how rigorous they are when selection at new personnel and see which aspects they fail or see that take more importance. The methodology to be used was the qualitative approach, basic type, with a descriptive method and no experimental that permit to analysis and study the environmental problems. According to mention before, the result that was obtained in the personnel selection processes for the operational area are performed in short- term and not always all the processes were used by the company, the ecological company needs to immediately to fill the position, so the work isn’t delayed and then be able to meet the specific deadlines that the company requests. Therefore, it’s recommended to the company train the personnel in the RR. HH area for this type of work, so the decisions taken it can be harmful, by collaborators who only stay a short-term or that collaborators that not find a solution for a present problem, especially in the technical part

Linking sustainability and the Fourth Industrial Revolution: a monitoring framework accounting for technological development

BackgroundIn this article, the concept of the Fourth Industrial Revolution and related implications for the measurement of sustainable development are analyzed. Technological innovations can play an important role in countering errant developments of the past and can support the transformation process towards a green economy in pursuit of the Sustainable Development Goals. On the other hand, they pose challenges to the social control of technology and represent a methodical quandary known as the Collingridge dilemma. The core statement of the dilemma is that the implications of new technologies will only be fully visible once they are embedded in socio-economic-ecological systems when the possibilities to control diminish. The main objective of this study is thus to develop a monitoring framework enabling the ex ante assessment of related technological shifts and their implications for sustainable development.ResultsTo approach the resulting difficulties for sustainability monitoring, digitization indicators should be accounted for in the German Sustainable Development Strategy. An enhanced strategy complemented by related Global Competitiveness Index 4.0 indicators, for which the Word Economic Forum assumes a modest link between competitiveness and inequality, illustrates the feasibility of linking research regarding the Fourth Industrial Revolution and sustainable development to measure its social and environmental consequences. The newly developed Sustainable Digital Socio-Economic-Ecological Indicator System categorizes the sustainability indicators into one index covering all Sustainable Development Goals along with four sub-indices emphasizing crucial aspects relevant to navigating a successful transformation. This novel and innovative approach is illustrated using the examples of Germany.ConclusionsThe Fourth Industrial Revolution is fundamentally driven by introducing renewable energy resources as a new energy regime. However, the effects extend beyond energy and necessitate comprehensive measurement frameworks for assessing sustainable development implications. This work contributes by analyzing the related impact on sustainable development and providing decision-makers with new insights for early recognition. Preliminary results for Germany expose a discrepancy between the status quo and the desired pathway, indicating emerging effects of the Fourth Industrial Revolution on inequality, employment, and education. While none of the sectors are sustainable, the sub-index analysis highlights distinct disparities among economic, social, and ecological sectors.

Global Potential Distribution of Sarcophaga dux and Sarcophaga haemorrhoidalis under Climate Change

Climate change has a direct impact on biodiversity, affecting ecosystems and altering their balance. Many taxa, including insects, are likely to be affected by climate change in terms of geographic distribution. Sarcophagid flies, such as Sarcophaga dux and Sarcophaga haemorrhoidalis, are important flies because of their apparent ecological, forensic, and medical significance. Global habitat suitability varies as a result of climate change. In wildlife management, models that predict species’ spatial distribution are being used more and more, which emphasizes the need for reliable methods to evaluate their accuracy. Consequently, the statistical robustness of maximum entropy was implemented in Maxent to model the current and future global distribution of both flies, involving occurrence data of 155 and 87 points for S. dux and S. haemorrhoidalis, respectively. Based on the Pearson correlation and Jackknife test, five bioclimatic variables were used for current and future predictive models. For future models, two representative concentration pathways (RCPs), 2.6 and 8.5, for 2050 and 2070 were applied. Both statistical parameters, AUC and TSS, were used to assess the resulting models with values equal to 0.80 (±0.01) and 0.9, respectively, for S. dux and equal to 0.86 (±0.01) and 0.92 for S. haemorrhoidalis. The resulting models for S. dux showed high and very high suitability in Europe, Tropical Africa, India, Canada, the United States from Alaska to Florida, Brazil, and Australia. In the case of S. haemorrhoidalis Europe and North and South America displayed low to medium suitability, but North Africa, including Egypt; Tropical Africa; Asia, including Saudi Arabia, India, and China; and Australia showed increased suitability. Decision-makers are put in conflict with their duties to avert destruction in the economic, medical, and ecological sectors by such anticipated models, and use these predictive models as a cornerstone for building a control strategy for such forensically important flies at local spatial scales.

Аналіз рішень та підходів кластеризації геопросторових даних для оптимізації продуктивності веб-карти та взаємодії користувача

In the contemporary epoch, the management and visualization of geospatial information in web browsers have gained substantial importance. Web maps are indispensable tools across various tourism, goods delivery, and ecology sectors. Furthermore, the extensive support of web browsers on diverse devices enhances the accessibility of geospatial data on the web for various users. However, the incessant increment of geospatial information poses new challenges in efficiently displaying data and navigation through these data on web maps. Therefore, the clustering of geospatial data is crucial in dealing with them. Different clustering methods may affect the performance or visual clarity of web maps. To improve the user experience and optimize the use of computing resources, geodata clustering becomes a necessary tool for processing large volumes of markers on the map. Despite significant progress in the development of geodata clustering solutions in web maps, there are some challenges that developers and users may encounter. In this article, challenges with scaling, dynamic cluster data, and heterogeneity of data are described. Existing problems in geodata clustering in web maps require additional research and development. Understanding these issues will help developers and researchers improve existing solutions and create new methods and approaches for efficient clustering of geodata in web maps. The urgency of solving the problem lies in the search for effective clustering solutions that provide an opportunity to ensure convenient interactivity and fast processing of geodata in web maps. This study provides a comprehensive review of data types and clustering methods. Tools and libraries for geodata clustering in web maps are analyzed. Different types of geodata and approaches to working with them were also studied. Concepts such as semi-static data and their positions alongside static and dynamic data types are elucidated. Through the analysis, optimal scenarios for applying specific clustering methods or the utilization of server-side clustering approaches have been identified. Conclusions have also been drawn on the preferred approach when handling extensive volumes of static or semi-static geospatial data, particularly advocating for the application of server-side clustering with caching. In conclusion, various clustering approaches in web maps, both client-side and server-side, have been scrutinized. The advantages and disadvantages of both approaches, along with recommendations on when to apply each method, have been delineated. A noticeable absence of explicit approaches in clustering vast geospatial data for web map representation underpins the relevance and necessity of research in this direction.

Ecological Implications of Land Use Dynamics in Kerala

Land is the predominant limited resource required for the development of agricultural sector. The philosophy and methods of land use in Kerala have changed over the past half century. The present research attempt to analyse the ecological implications of land use dynamics in Kerala. Intra and inter-sectoral budgeting of the nine land use classes were done for the period from 1985 to 2019. The intra-sectoral budgeting indicated that within the ecological sector, the major land use shifts occurred from barren and uncultivable land to other sub-sectors. The inter-sectoral budgeting analysis showed that with an exception in period IV, the area under the non-agricultural sector exhibited a substantial increase in Kerala in the remaining periods as well as overall period.

THE EXISTENCE OF ALBASIA PLANTS IN CIKARAMAS VILLAGE: A CASE STUDY OF ALBASIA CULTIVATION IN CIKARAMAS VILLAGE, SUMEDANG REGENCY, WEST JAVA

With forestry development, it is hoped that national development goals will be achieved, in particular the improvement of living standards and community welfare accompanied by improvement of their living environment. Wood is one of the main forest products, which is needed by the community to meet various needs of human life. Although nowadays there are many synthetic materials that can substitute the use of wood, the demand for wood for various purposes is still quite high and even shows an increasing demand. The benefits of this wood are growing, starting from the need for wood for energy, building materials, wood pulp, furniture, to the use of wood for aircraft fuselages. The planting of sengon trees by the community in Cikaramas Village is quite developed and is very suitable considering the topography and land conditions that are suitable for the place where sengon trees grow. Then it becomes a separate added value, if farmers/communities are able to optimize inputs for forest plant cultivation by knowing in advance the characteristics/characteristics of the soil. Albasia that developed in Cikaramas Village, is part of the knowledge system of Cikaramas Village residents about timber plants. The system is a legacy of knowledge about the types and uses of woody plants for subsistence needs used for building materials. Prior to the development of albasia, wood plants of the type of tissue, jackfruit wood, and the type of "swamp" wood had been developed and used for the needs of previous building materials. Then the development of albasia plant species, causing choices in developing woody plants to be utilized and to be developed in more intercropping systems. This allows farmers to choose based on their knowledge and experience, and choose based on the benefits and advantages derived from the timber plant, which will be developed in an intercropping system. This study uses descriptive qualitative research, researchers try to get a picture of the ongoing state of the object of research according to the actual situation, so that accurate data can be obtained with an actual picture from the analysis of the observed phenomena. Through this research, it is hoped that data will be obtained and presented through the use of words to obtain a more concrete, detailed and accurate description of certain phenomena. The development and use of Albasia in Cikaramas Village is a form of adaptation carried out by the community that responds to environmental suitability and changes. Response to information or ideas that enter the social system of Cikaramas Village, which has an influence on the ecological system of Cikaramas Village. And the socio-economic activities of Albasia gave birth to the Albasia trading network. As well as the survival of Albizia as a commodity crop under the influence of the threat of crop failure due to pests, factored in by economic sector and ecological sector.

Single-frame 3D lensless microscopic imaging via deep learning.

Since the pollen of different species varies in shape and size, visualizing the 3-dimensional structure of a pollen grain can aid in its characterization. Lensless sensing is useful for reducing both optics footprint and cost, while the capability to image pollen grains in 3-dimensions using such a technique could be truly disruptive in the palynology, bioaerosol sensing, and ecology sectors. Here, we show the ability to employ deep learning to generate 3-dimensional images of pollen grains using a series of 2-dimensional images created from 2-dimensional scattering patterns. Using a microscope to obtain 3D Z-stack images of a pollen grain and a 520 nm laser to obtain scattering patterns from the pollen, a single scattering pattern per 3D image was obtained for each position of the pollen grain within the laser beam. In order to create a neural network to transform a single scattering pattern into different 2D images from the Z-stack, additional Z-axis information is required to be added to the scattering pattern. Information was therefore encoded into the scattering pattern image channels, such that the scattering pattern occupied the red channel, and a value indicating the position in the Z-axis occupied the green and blue channels. Following neural network training, 3D images were formed from collated generated 2D images. The volumes of the pollen grains were generated with a mean accuracy of ∼84%. The development of airborne-pollen sensors based on this technique could enable the collection of rich data that would be invaluable to scientists for understanding mechanisms of pollen production climate change and effects on the wider public health.

Climate Change in the Provenance Regions of Romania over the Last 70 Years: Implications for Forest Management

The recent climate change scenarios show significant increases in temperature and extreme drought events in Southern and Eastern Europe by the end of the 21st century, which will have a serious impact on forest growth and adaptation, and important consequences for forest management. The system of provenance regions, according to the OECD Scheme and EU Directive, was thought to encourage the use of the local seed sources, under the concept ‘local is the best’. However, climate is changing faster than some species or populations can adapt or migrate, which raises some uncertainties with respect to the future performance of local populations. In Romania, as in other countries, the delimitation of provenance regions is based on geographical, ecological and vegetation criteria. The aim of this study is to evaluate: (1) the climate change that has occurred at the level of the provenance regions; (2) which regions will be most vulnerable to climate change; (3) which forest types will be the most vulnerable in a certain region; and (4) changes in the climatic envelope of forest species. Several climatic parameters and an ecoclimatic indices have been calculated and analyzed at the level of provenance regions, subregions and ecological sectors (forest types) in Romania, during the period 1951–2020. The results highlight a general shift towards warmer and drier conditions in the last 30 years, the mean annual temperature increasing with 0.3–1.1 °C across the provenance subregions. The De Martonne aridity index for the vegetation season shows that 86% of the ecological sectors fell into the arid and semiarid categories, which indicates a very high degree of vulnerability for forest species. On the Lang rainfall index, forest steppe climatic conditions occurred in all pure or mixed pedunculate oak forests, thermophile oak species, meadow forests, poplar and willow, Turkey oak and Hungarian oak forests. The Ellenberg coefficient highlights that the warming process is more evident along the altitude and the degree of vulnerability increase at lower altitude or at the edge of species distribution. The climate envelopes of many forest species have already shifted to another ecosystem’s climate. This paper presents the importance of re-delineation the provenance regions for the production and deployment of forest reproductive materials according to the climate change occurred in the last decades, as a fundamental tool for an adaptive forest management.

Evaluation of Climate Change Impacts on the Global Distribution of the Calliphorid Fly Chrysomya albiceps Using GIS

Climate change is expected to influence the geographic distribution of many taxa, including insects. Chrysomya albiceps is one of the most pervasive calliphorid fly with apparent ecological, forensic, and medical importance. However, the global habitat suitability is varied due to climate change. Models that forecast species spatial distribution are increasingly being used in wildlife management, highlighting the need for trustworthy techniques to assess their accuracy. So, we used the maximum entropy implemented in Maxent to predict the current and future potential global geographic distribution of C. albiceps and algorithms of DIVA-GIS to confirm the predicted current model. The Maxent model was calibrated using 2177 occurrence records. Based on the Jackknife test, four bioclimatic variables along with altitude were used to develop the final models. For future models, two representative concentration pathways (RCPs), 2.6 and 8.5, for 2050 and 2070 were used. The area under curve (AUC) and true skill statistics (TSS) were used to evaluate the resulted models with values equal to 0.92 (±0.001) and 0.7, respectively. Two-dimensional niche analysis illustrated that the insect can adapt to low and high temperatures (9 °C to 27 °C), and the precipitation range was 0 mm to 2500 mm. The resulted models illustrated the global distribution of C. albiceps with alteration to its distribution in the future, especially on the Mediterranean coasts of Europe and Africa, Florida in the USA, and the coasts of Australia. Such predicted shifts put decision makers against their responsibilities to prevent destruction in economic, medical, and ecological sectors.

Research of Carbon Emission Reduction Potentials in the Yellow River Basin, Based on Cluster Analysis and the Logarithmic Mean Divisia Index (LMDI) Method

China has implemented many green transition policies to reach its carbon peak target, some of which do not consider the actual carbon reduction pressures that localities can afford, thus lowering the living standards of residents and economic growth, which makes the green transition process unsustainable. The Yellow River Basin plays an important role in China’s energy, food, manufacturing, and ecological sectors. Thus, the design of green transition policies in the region needs to be modest and efficient. Based on the data of 100 prefecture-level cities in the Yellow River Basin from 2006 to 2017, this paper uses the K-means clustering to divide the carbon reduction potential of cities into four types. Most cities’ carbon reduction potentials are low or medium, unsuitable for adopting a rapid green transition. Based on the logarithmic mean Divisia index (LMDI) decomposition results and the carbon reduction potential, we designed different carbon-control pathways: Shandong and Henan should focus on increasing investment in green technology, especially oxy-combustion technology; Gansu, Ningxia, and Qinghai could partially offset carbon emissions through land use, land-use change and forestry (LULUCF) activities; Sichuan and Inner Mongolia should increase their energy-use efficiency; Shaanxi and Shanxi could use green finance to complete the upgrading of local industries. The above emission-reduction strategies can be actively pursued in cities with high emission reduction potential and should be implemented with caution in cities with low emission reduction potential. This paper provides a new and cost-effective perspective on carbon emission control in the Yellow River Basin.

Dynamics of Land Use Trends and Patterns in Uttar Pradesh: A Sectoral Perspective

Land is a natural resource and is of utmost importance on account of its use for diverse and manifold purposes. However, this finite resource has been experiencing changes in the way it was used earlier, largely because of modifications made by human, affecting the ability of natural systems to support life. The present study is carried out for Uttar Pradesh, one of the states in India. It is the fourth largest in area and interestingly having the largest rural population in the country. Moreover, this Uttar Pradesh has been going through large-scale land use transformations, particularly after economic reforms. Thus, this study made an attempt to understand the dynamics of trends and patterns of land use in Uttar Pradesh, as it is directly or indirectly associated with their living. The study largely focused on three sectors for analysing this status: ecological, agricultural and non-agricultural. It primarily focused on the secondary sources of data, incorporating various government publications from 2000 to 2015. Multiple linear regression technique was executed to examine the scenario of sectoral land use and to find the determinants of land use changes. The findings of this study reveal that the growth rates of different categories of land use have witnessed declining trends except for the area under non-agricultural land use. Whereas enviable ecological sector is passing through a critical phase of land transformation as the area under forest is declining along with pastures and miscellaneous trees. It also points out considerable decrease for net sown area in agricultural sector but increase for current fallow land.

Analysis of the Water Demand-Supply Gap and Scarcity Index in Lower Amu Darya River Basin, Central Asia.

Lower reaches of the Amu Darya River Basin (LADB) is one of the typical regions which is facing the problem of water shortage in Central Asia. During the past decades, water resources demand far exceeds that supplied by the mainstream of the Amu Darya River, and has resulted in a continuous decrease in the amount of water flowing into the Aral Sea. Clarifying the dynamic relationship between the water supply and demand is important for the optimal allocation and sustainable management of regional water resources. In this study, the relationship and its variations between the water supply and demand in the LADB from the 1970s to 2010s were analyzed by detailed calculation of multi-users water demand and multi-sources water supply, and the water scarcity indices were used for evaluating the status of water resources utilization. The results indicated that (1) during the past 50 years, the average total water supply (TWS) was 271.88 × 108 m3/y, and the average total water demand (TWD) was 467.85 × 108 m3/y; both the volume of water supply and demand was decreased in the LADB, with rates of −1.87 × 108 m3/y and −15.59 × 108 m3/y. (2) percentages of the rainfall in TWS were increased due to the decrease of inflow from the Amu Darya River; percentage of agriculture water demand was increased obviously, from 11.04% in the 1970s to 44.34% in 2010s, and the water demand from ecological sector reduced because of the Aral Sea shrinking. (3) the supply and demand of water resources of the LADB were generally in an unbalanced state, and water demand exceeded water supply except in the 2010s; the water scarcity index decreased from 2.69 to 0.94, indicating the status changed from awful to serious water scarcity. A vulnerable balanced state has been reached in the region, and that water shortages remain serious in the future, which requires special attention to the decision-makers of the authority.

Modeling the interaction of participants in the innovation process in Ecological Sector

As part of the innovation process, including ecological sector, the characteristics of the interaction of participants in the chain “producer-intermediary-consumer” are considered. The possible influence of companies promoting innovative products, which play the role of an intermediary and are a link between the manufacturer and the consumer, on the perception of the manufacturer’s innovative products by the end user has been evaluated. The features of the activities of specialized companies to promote innovative products to the market have been investigated. Based on the use of the theory of expectations by Viktor Vrum, a mathematical model is proposed that describes the effectiveness of interaction between the participants in the innovation process within the “producer-intermediary-consumer” chain, which makes it possible to assess the quality of the activities of specialized companies in promoting innovative products to the market including ecological and energy sector.

Community perceptions and conceptions of the impact of forest and land fires in Bengkalis Regency, Riau Province

Forest and land fires have a very bad impact on the economic, social, health and ecological sectors. Forest and land fires in Bengkalis Regency are influenced by several factors, including human Negligence, and land contour factors, according to BPPT 69.69% of the Bengkalais area is a peat area that is prone to decreasing water levels causing drought. Drought on peatlands triggers forest and land fires in the dry season. This research was conducted to determine the community’s perception and conception of the impact of forest and land fires. Primary data and research information were obtained through observation and interviews. The research sample was obtained from 12 villages from 4 sub-districts in Bengkalis Regency, the names of the villages are Bukit Kerikil, Tanjung Leban, Sepahat, Temiang, Bad Bakul, Dompas, Muara Dua, Bandar Jaya, Sumber Jaya, Tasik Serai Barat, East Serai Lake, Cliff Lake. Lemongrass. Respondents who have been interviewed as many as 120 people. Data analysis was done descriptively. From the interviews conducted, it was found that the public perception was quite high. Communities understand that their participation in and monitoring of forest and land use is critical to reducing forest and land fires.

Ecological Mitigation and Earth Restoration Strategies in the COVID-19 Post-Pandemic Era

The COVID-19 pandemic has been existing for more than a year, and nearly all sectors have been affected by this condition, including the ecological sector. This study explores the ecological facts amidst the conditions of COVID-19 pandemic and aims to review some strategies to maintain the improved ecological conditions up to the post-pandemic era. This paper is a result of qualitative exploration based on secondary data and literature study. The analysis of the topic leads to some conclusions, including that the sharp decline in human activities due to the COVID-19 pandemic has brought a positive impact on the ecological condition, making it more natural and healthier. The most significant improvement in the ecological condition of the earth during the pandemic is the reduced air pollution. Meanwhile, the negative impact on ecology includes the increasing volume of plastic waste and medical waste. The outbreak of COVID-19 may not be the last pandemic faced by humans if they continue to destroy the nature. Good behavior that results in reduced pollution during the pandemic should be maintained and preserved in the COVID-19 post-pandemic future.

Optimal Allocation of Surface Water Resources at the Provincial Level in the Uzbekistan Region of the Amudarya River Basin

Water users in the Amudarya River Basin in Uzbekistan are suffering severe water use competition and uneven water allocation, which seriously threatens ecosystems, as shown, for example, in the well-known Aral Sea catastrophe. This study explores the optimized water allocation schemes in the study area at the provincial level under different incoming flow levels, based on the current water distribution quotas among riparian nations, which are usually ignored in related research. The optimization model of the inexact two-stage stochastic programming method is used, which is characterized by probability distributions and interval values. Results show that (1) water allocation is redistributed among five different sectors. Livestock, industrial, and municipality have the highest water allocation priority, and water competition mainly exists in the other two sectors of irrigation and ecology; (2) water allocation is redistributed among six different provinces, and allocated water only in Bukhara and Khorezm can satisfy the upper bound of water demand; (3) the ecological sector can receive a guaranteed water allocation of 8.237–12.354 km3; (4) under high incoming flow level, compared with the actual water distribution, the total allocated water of four sectors (except for ecology) is reduced by 3.706 km3 and total economic benefits are increased by USD 3.885B.