The journals’ author guidelines and/or editorial policies were examined on whether they take a stance with regard to the availability of the underlying data of the submitted article. The mere explicated possibility of providing supplementary material along with the submitted article was not considered as a research data policy in the present study. Furthermore, the present article excluded source codes or algorithms from the scope of the paper and thus policies related to them are not included in the analysis of the present article.

For selection of journals within the field of neurosciences, Clarivate Analytics’ InCites Journal Citation Reports database was searched using categories of neurosciences and neuroimaging. From the results, journals with the 40 highest Impact Factor (for the year 2017) indicators were extracted for scrutiny of research data policies. Respectively, the selection journals within the field of physics was created by performing a similar search with the categories of physics, applied; physics, atomic, molecular & chemical; physics, condensed matter; physics, fluids & plasmas; physics, mathematical; physics, multidisciplinary; physics, nuclear and physics, particles & fields. From the results, journals with the 40 highest Impact Factor indicators were again extracted for scrutiny. Similarly, the 40 journals representing the field of operations research were extracted by using the search category of operations research and management.

Journal-specific data policies were sought from journal specific websites providing journal specific author guidelines or editorial policies. Within the present study, the examination of journal data policies was done in May 2019. The primary data source was journal-specific author guidelines. If journal guidelines explicitly linked to the publisher’s general policy with regard to research data, these were used in the analyses of the present article. If journal-specific research data policy, or lack of, was inconsistent with the publisher’s general policies, the journal-specific policies and guidelines were prioritized and used in the present article’s data. If journals’ author guidelines were not openly available online due to, e.g., accepting submissions on an invite-only basis, the journal was not included in the data of the present article. Also journals that exclusively publish review articles were excluded and replaced with the journal having the next highest Impact Factor indicator so that each set representing the three field of sciences consisted of 40 journals. The final data thus consisted of 120 journals in total.

‘Public deposition’ refers to a scenario where researcher deposits data to a public repository and thus gives the administrative role of the data to the receiving repository. ‘Scientific sharing’ refers to a scenario where researcher administers his or her data locally and by request provides it to interested reader. Note that none of the journals examined in the present article required that all data types underlying a submitted work should be deposited into a public data repositories. However, some journals required public deposition of data of specific types. Within the journal research data policies examined in the present article, these data types are well presented by the Springer Nature policy on “Availability of data, materials, code and protocols” (Springer Nature, 2018), that is, DNA and RNA data; protein sequences and DNA and RNA sequencing data; genetic polymorphisms data; linked phenotype and genotype data; gene expression microarray data; proteomics data; macromolecular structures and crystallographic data for small molecules. Furthermore, the registration of clinical trials in a public repository was also considered as a data type in this study. The term specific data types used in the custom coding framework of the present study thus refers to both life sciences data and public registration of clinical trials. These data types have community-endorsed public repositories where deposition was most often mandated within the journals’ research data policies.

The term ‘location’ refers to whether the journal’s data policy provides suggestions or requirements for the repositories or services used to share the underlying data of the submitted works. A mere general reference to ‘public repositories’ was not considered a location suggestion, but only references to individual repositories and services. The category of ‘immediate release of data’ examines whether the journals’ research data policy addresses the timing of publication of the underlying data of submitted works. Note that even though the journals may only encourage public deposition of the data, the editorial processes could be set up so that it leads to either publication of the research data or the research data metadata in conjunction to publishing of the submitted work.

Nature Genetics Impact Factor 2024-2025 - ResearchHelpDesk - Nature Genetics is a monthly journal publishing the best research from across the field of genetics, with our broad scope ensuring that work published reaches the widest possible audience. All editorial decisions are made by a team of full-time professional editors. Nature Genetics publishes the very highest quality research in genetics. It encompasses genetic and functional genomic studies on human and plant traits and on other model organisms. Current emphasis is on the genetic basis for common and complex diseases and on the functional mechanism, architecture and evolution of gene networks, studied by experimental perturbation. Integrative genetic topics comprise, but are not limited to: Genes in the pathology of human disease Molecular analysis of simple and complex genetic traits Cancer genetics Agricultural genomics Developmental genetics Regulatory variation in gene expression Strategies and technologies for extracting function from genomic data Pharmacological genomics Genome evolution

Nature Biotechnology Impact Factor 2024-2025 - ResearchHelpDesk - Nature Biotechnology is interested in the best research from across the field of Biotechnology; our broad scope ensures that work published reaches the widest possible audience. All editorial decisions are made by a team of full-time professional editors. Nature Biotechnology is a monthly journal covering the science and business of biotechnology. It publishes new concepts in technology/methodology of relevance to the biological, biomedical, agricultural and environmental sciences as well as covers the commercial, political, ethical, legal, and societal aspects of this research. The first function is fulfilled by the peer-reviewed research section, the second by the expository efforts in the front of the journal. We provide researchers with news about business; we provide the business community with news about research developments. The core areas in which we are actively seeking research papers include: molecular engineering of nucleic acids and proteins; molecular therapy (therapeutics genes, antisense, siRNAs, aptamers, DNAzymes, ribozymes, peptides, proteins); large-scale biology (genomics, functional genomics, proteomics, structural genomics, metabolomics, etc.); computational biology (algorithms and modeling), regenerative medicine (stem cells, tissue engineering, biomaterials); imaging technology; analytical biotechnology (sensors/detectors for analytes/macromolecules), applied immunology (antibody engineering, xenotransplantation, T-cell therapies); food and agricultural biotechnology; and environmental biotechnology. A comprehensive list of areas of interest is shown below. Strategies for controlling gene expression Strategies for manipulating gene structure Strategies for gene containment Technologies for analyzing gene function (e.g., arrays, SAGE) Technologies for analyzing gene structure/organization (e.g., molecular beacons) Chemogenomics or chemical genetics Pharmacogenomics/SNPs Computational analysis Technologies for analyzing/identifying protein structure/function (e.g., 2-D gels, mass spectrometry, yeast two-hybrid, SPR, NMR, arrays and chips) Structural genomics Computational analysis Technologies for analyzing/profiling metabolites (chromatography, mass spectrometry) Computational analysis Bioinformatics; algorithms; data deconvolution Modeling and systems biology: kinetics-based models and constraints-based models Rational approaches for proteins/antibodies/enzymes/drugs Molecular evolution Molecular breeding approaches Genetic manipulation of species of interest to modify or allow the production of a commercially or therapeutically relevant compound Computational analysis Mammalian cells Insect cells Bacteria Fungi Plant cells Targeting strategies Viral and nonviral vector strategies Reporter molecules Imaging approaches/technologies for visualizing whole animals, cells, or single molecules Computational analysis Gene therapy (targeting, expression, integration, immunogenicity) Antisense RNAi DNAzymes and ribozymes Nanomaterials for use in drug delivery or as therapeutics Nanomaterials for use in industrial biotechnology Nanosensors Nanosystems for imaging molecules and cells Antibody engineering T-cell therapies Therapies exploiting innate immunity (e.g. complement) Antigen delivery vectors and approaches Nucleic acid vaccines Computational analysis Stem cells Tissue engineering Therapeutic cloning (somatic cell nuclear transfer) Xenotransplantation Biomaterials Approaches for detecting biological molecules Use of biological systems in detecting analytes Approaches for multiplexing and increasing throughput Selection/screening strategies for gene/proteins/drugs Microfluidics Engineering materials for biological application Molecular imprinting Biomimetics Nanotechnology Crop improvement (resistance to stress, disease, pests) Nutraceuticals Forest biotechnology Plant vaccines Plants as bioreactors Gene-containment strategies Transgenic animals Knockouts Reproductive cloning Biopharmaceutical and enzyme production Transgene targeting and expression strategies Bioremediation Biomining Phytoremediation Monitoring

Nature Data Management Platforms Market Outlook

According to our latest research, the global Nature Data Management Platforms market size in 2024 is valued at USD 2.1 billion, with a robust compound annual growth rate (CAGR) of 13.6% anticipated from 2025 to 2033. By the end of 2033, the market is forecasted to reach approximately USD 6.6 billion. This impressive growth trajectory is driven by increasing global emphasis on biodiversity conservation, the rising adoption of digital technologies in environmental monitoring, and the growing need for integrated data solutions to support sustainable development initiatives.

One of the most significant growth factors propelling the Nature Data Management Platforms market is the escalating global awareness regarding environmental degradation and biodiversity loss. Governments, NGOs, and private enterprises are increasingly recognizing the critical importance of preserving natural habitats and ecosystems. This realization has led to the proliferation of numerous biodiversity monitoring programs and conservation initiatives that require advanced data management solutions. Nature Data Management Platforms enable stakeholders to collect, store, analyze, and share vast amounts of ecological data in real-time, facilitating informed decision-making and effective policy formulation. As the demand for transparent and actionable environmental data intensifies, these platforms are becoming indispensable tools for organizations striving to achieve their sustainability goals.

Another key driver for the Nature Data Management Platforms market is the rapid advancement in digital technologies, particularly cloud computing, artificial intelligence, and the Internet of Things (IoT). These technological innovations have revolutionized how environmental data is captured, processed, and utilized. Cloud-based platforms offer unparalleled scalability, flexibility, and accessibility, allowing users to manage and analyze complex datasets from remote locations. Integration with AI and machine learning algorithms further enhances the capabilities of these platforms by enabling predictive analytics, automated data classification, and anomaly detection. This technological synergy not only streamlines data management processes but also unlocks new opportunities for research, conservation planning, and environmental impact assessment, thereby fueling market growth.

The increasing regulatory pressures and the need for compliance with international environmental standards are also contributing to the expansion of the Nature Data Management Platforms market. Regulatory bodies across the globe are implementing stringent reporting requirements and mandating the use of standardized data management practices for environmental monitoring and conservation projects. This regulatory landscape is compelling organizations to invest in robust data management platforms that can ensure data integrity, security, and compliance. Furthermore, the growing trend of public-private partnerships in environmental conservation is fostering the adoption of these platforms among a diverse set of end-users, including government agencies, research institutions, and enterprises. As a result, the market is witnessing a surge in demand for customized and scalable solutions tailored to the unique needs of different stakeholders.

From a regional perspective, North America currently dominates the Nature Data Management Platforms market, accounting for the largest share in 2024. This leadership position is attributed to the region's advanced technological infrastructure, strong regulatory framework, and the presence of several leading market players. Europe follows closely, driven by robust environmental policies and significant investments in biodiversity conservation projects. The Asia Pacific region is poised for the fastest growth during the forecast period, owing to increasing government initiatives, rapid urbanization, and heightened awareness about environmental sustainability. Latin America and the Middle East & Africa are also witnessing gradual adoption of nature data management solutions, supported by international collaborations and funding for conservation efforts. Collectively, these regional dynamics underscore the global momentum towards leveraging data-driven approaches for sustainable environmental management.

Academic publishers purport to be arbiters of knowledge, aiming to publish studied that advance the frontiers of their research domain. Yet the effectiveness of journal editors at identifying novel and important research is generally unknown, in part because of the confidential nature of the editorial and peer-review process. Using questionnaires, we evaluated the degree to which journals are effective arbiters of scientific impact in the domain of Ecology, quantified by three key criteria. First, journals discriminated against low-impact manuscripts: the probability of rejection increased as the number of citations gained by the published paper decreased. Second, journals were more likely to publish high-impact manuscripts (those that obtained citations in 90th percentile for their journal) than run-of-the-mill manuscripts; editors were only 23 and 41% as likely to reject an eventual high-impact paper (pre- versus post-review rejection) compared to a run-of-the-mill paper. Third, editors did occasionally reject papers that went on to be highly cited. Error rates were low, however: only 3.8% of rejected papers gained more citations than the median article in the journal that rejected them, and only 9.2% of rejected manuscripts went on to be high-impact papers in the (generally lower impact factor) publishing journal. The effectiveness of scientific arbitration increased with journal prominence, although some highly prominent journals were no more effective than much less prominent ones. We conclude that the academic publishing system, founded on peer review, appropriately recognises the significance of research contained in manuscripts, as measured by the number of citations that manuscripts obtain after publication, even though some errors are made. We therefore recommend that authors reduce publication delays by choosing journals appropriate to the significance of their research.

Environmental Impact Statement: Geological research and mapping in Mootwingee National Park and Coturaundee Nature Reserve : review of environmental factors

Abstract English is the lingua franca for scientific communication, but some journals, especially in developing countries, still publish non-English studies. A shift towards publishing in English may promote internationalization and more visibility of scientific journals. Here we compared quality indexes between Brazilian journals that have always published in English and journals that have published in languages other than English. We also investigated whether a temporal shift towards publishing in English led to elevated quality measures. Our analyses covered 16 Brazilian biodiversity journals and accounted for 12640 papers published since 2007. The mean impact factor was on average 55% higher in journals that have published consistently in English, compared to the so-called multilanguage journals. The proportion of publications in English increased to nearly three times the original value in multilanguage journals between 2007 and 2016, and the impact factor tripled during this period. At the same time, the Qualis-Capes classifications (B1-B2-B3) tended to fall. Publishing in English can be a first step to increased visibility, and this is particularly important for biodiversity journals, since Brazilian ecosystems are considered of interest to the international scientific community and nature conservation.

Nature‑Related Risk Analytics Market Outlook

According to our latest research, the global Nature-Related Risk Analytics market size reached USD 1.48 billion in 2024, reflecting the rapid adoption of advanced analytics tools across industries to address environmental and nature-related risks. The market is expected to grow at a robust CAGR of 17.2% from 2025 to 2033, projecting a value of USD 6.18 billion by 2033. This growth is primarily driven by the increasing regulatory scrutiny, rising environmental awareness, and the need for organizations to proactively manage risks associated with biodiversity loss, climate change, and ecosystem degradation.

The expansion of the Nature-Related Risk Analytics market is significantly influenced by the evolving global regulatory landscape. Governments and international bodies are introducing stringent reporting requirements and sustainability frameworks, such as the Taskforce on Nature-related Financial Disclosures (TNFD) and the EU Taxonomy, which compel organizations to assess, report, and mitigate their nature-related risks. This regulatory momentum is pushing enterprises to adopt sophisticated analytics platforms that can provide real-time insights into their environmental footprint and exposure to nature-related risks. Moreover, the integration of these analytics tools into enterprise risk management frameworks is enabling organizations to make data-driven decisions, enhance transparency, and maintain compliance with emerging global standards.

Another critical growth factor is the increasing recognition among corporations of the financial implications associated with nature-related risks. Companies are now acknowledging that biodiversity loss, deforestation, water scarcity, and other ecosystem disruptions can directly impact their supply chains, operations, and long-term profitability. As a result, there is a surge in demand for comprehensive risk analytics solutions that can quantify, model, and forecast the financial impact of nature-related events. These solutions not only help organizations identify vulnerabilities but also enable them to implement mitigation strategies and build resilience against environmental shocks. The growing emphasis on environmental, social, and governance (ESG) investing further amplifies the need for robust nature-related risk analytics, as investors increasingly factor ecological risks into their decision-making processes.

Technological advancements are also playing a pivotal role in the market’s growth trajectory. The integration of artificial intelligence (AI), machine learning (ML), geospatial analytics, and big data technologies has revolutionized nature-related risk analytics by enabling the processing of vast and complex environmental datasets. These innovations facilitate real-time monitoring, predictive modeling, and scenario analysis, empowering organizations to anticipate and respond to environmental threats proactively. Additionally, the proliferation of cloud-based analytics platforms is making nature-related risk analytics more accessible and scalable, allowing organizations of all sizes to leverage advanced risk assessment tools without significant upfront investments in infrastructure.

From a regional perspective, North America and Europe are leading the adoption of nature-related risk analytics solutions, driven by stringent regulatory frameworks, high environmental consciousness, and the presence of large multinational corporations. Asia Pacific is emerging as a high-growth region, propelled by rapid industrialization, increasing climate-related risks, and growing government initiatives focused on sustainable development. Latin America and the Middle East & Africa are also witnessing gradual uptake, particularly in sectors such as energy, agriculture, and mining, where nature-related risks are pronounced. The global nature-related risk analytics market is thus characterized by dynamic regional trends, with each region exhibiting unique drivers and challenges that shape the adoption and evolution of risk analytics solutions.

Research in protected areas (PAs) is often dominated by scientists from outside the conservation agencies managing them. This can potentially lead to misalignment with local needs, insensitivity to the local context, and a lack of investment in and use of local expertise. These issues often arise when international researchers work in another country without local engagement (known as “parachute science”). Despite PAs being key end-users of actionable science, there is limited understanding of the prevalence and impact of parachute science in these areas. Here, we investigate parachute versus collaborative research in two national parks in the Global South (Kruger National Park, South Africa; Nahuel Huapi National Park, Argentina), and one park from a developed economy (Kakadu National Park, Australia). To explore the prevalence, risks, benefits, and complexities of research practices, we analyse patterns of authorship, funding, and acknowledgment in a random sample of peer-reviewed papers from research conducted in these parks. Our findings show a higher incidence of potential parachute science in Kruger National Park (18% of papers with only out-of-country authors) compared to Nahuel Huapi (4%) and Kakadu (2%) National Parks. However, the occurrence of internationally collaborative research (national and international authors) was double in Global South parks (35-38%) than in the Australian park (18%). The study illustrates the potential benefits of international collaboration for PAs, including increased research productivity, expanded funding sources, and possibly higher impact and visibility of published studies. PAs in developed countries may have fewer opportunities to obtain those benefits. Most papers, even those with in-country authors, lacked authors affiliated with the agency managing the PA, and often failed to even acknowledge these agencies. This suggests the potential for a different form of parachute science (which we term “park parachuting”) in which lack of local involvement may hamper integration of research with management. Synthesis and applications: Establishing conditions that foster collaboration between national and international researchers, and between PA agency staff and external researchers (regardless of their nationality), would enable parks to better serve as catalysts for research collaboration. This collaborative approach can facilitate access to additional funding, enhance research capacity, increase research productivity, and amplify research impact. Methods Literature search strategy and inclusion criteria Literature searches were conducted via the Clarivate Web of Science search facility using the “all databases” (which includes the Web of Science Core Collection, SciELO, BIOSIS Previews and others) and “all editions” options. The search strings consisted of the name of the park [(“Kruger National Park”); (“Nahuel Huapi National Park” OR “Parque Nacional Nahuel Huapi” OR “Lake Nahuel Huapi” OR “Lago Nahuel Huapi”); (“Kakadu National Park”)] and we selected the “topic” as search field. According to Clarivate, the “topic” field searches for the specified search terms in the title, abstract, or keywords, and therefore will miss papers using the park name in the main text only. We restricted the search to journal articles published between 2010 and 2020. Each article was assigned a random number. We then assessed the relevant papers in ascending order until we reached a maximum of 100 relevant papers. Although parachute science practices appear to be more prevalent in publications in English than in those in other languages (Miller et al., 2023), there is a growing awareness of the detrimental effects of ignoring non-English publications (Amano et al., 2021). Therefore, we included papers for Nahuel Huapi published in Spanish (13% of our random sample). To be included, studies needed to be based on research within the park, have made use of data or specimens collected from the park, or have used the park as a case study in descriptive studies. Articles were also included if the park formed a central component in social-ecological studies (e.g., community relationships, human-wildlife conflict or livestock/wildlife diseases around parks). Articles making a passing reference to a park, or referencing research or other papers from a park, were excluded. Data extraction and analysis Metadata for all authors was extracted, including author rank (primary or co-author) and their primary institutional affiliation. Author affiliations were coded based on (i) nationality (country of all authors’ primary affiliation) and (ii) managing agency (whether or not an author was affiliated with the respective national park agency). In addition, the funding sources for the research were extracted; funding sources were most commonly listed within the acknowledgements section (Cronin, 2001; Mejia, & Kajikawa, 2018), or as a separate funding statement. We classified the funding as originating from the country where the national park was located (“national funding”), or from another country (“international funding”). References to non-financial contributions to the research by the park agency, such as logistical support or field assistance, mentioned in the acknowledgements section were also extracted. We did not consider a mention of a park-issued permit as reflecting genuine acknowledgement of engagement, but rather as an acknowledgement of compliance with conditions required to gain access to the parks. To estimate the academic ranking of the journal where research was published and thus the potential academic visibility and reach of a study, we used the Clarivate journal impact factor (2021). In cases where no impact factor was assigned, we recorded a zero. We also calculated the average number of citations per year as a metric of the paper’s uptake and impact in the literature. We calculated this by dividing the total number of citations recorded in Clarivate Web of Science by the number of years since publication up to 2023. References

Amano, T., Rios Rojas, C., Boum II, Y., Calvo, M., & Misra, B.B. (2021). Ten tips for overcoming language barriers in science. Nature Human Behaviour, 5(9), 1119-1122. Cronin, B. (2001). Acknowledgement trends in the research literature of information science. Journal of Documentation, 57(3), 427–433. Mejia, C. & Kajikawa, Y. (2018). Using acknowledgement data to characterize funding organizations by the types of research sponsored: The case of robotics research. Scientometrics, 114 (3), 883–904.

Data Warehousing Market Outlook

According to our latest research, the global data warehousing market size reached USD 29.7 billion in 2024, reflecting robust demand across a range of industries. Driven by the growing need for advanced analytics, real-time data integration, and scalable storage solutions, the market is expected to register a CAGR of 9.2% during the forecast period. By 2033, the market size is projected to reach USD 65.2 billion, underscoring the transformative impact of data-driven decision-making and digital transformation initiatives worldwide. The expansion is propelled by the proliferation of big data, cloud adoption, and the increasing complexity of business operations as organizations strive for enhanced agility and competitiveness.

A significant growth factor for the data warehousing market is the accelerating adoption of cloud-based solutions. Enterprises are increasingly migrating from traditional on-premises data warehouses to cloud-native platforms due to their scalability, cost-effectiveness, and ability to handle vast volumes of structured and unstructured data. The flexibility offered by cloud deployment enables organizations to scale resources dynamically based on workload demands, driving operational efficiencies and reducing capital expenditures. Furthermore, the integration of artificial intelligence and machine learning within cloud data warehouses is empowering businesses to extract actionable insights, automate data management tasks, and support predictive analytics, further fueling market growth.

Another key driver is the surge in demand for advanced analytics and business intelligence tools. As organizations recognize the value of data-driven decision-making, there is a heightened focus on leveraging data warehousing solutions to consolidate disparate data sources, enable real-time analytics, and foster collaboration across business units. The rise of self-service analytics platforms and intuitive data visualization tools is democratizing data access, allowing non-technical users to generate insights independently and accelerating the pace of innovation. Additionally, regulatory compliance and data governance requirements are compelling enterprises to invest in robust data warehousing infrastructure to ensure data accuracy, security, and traceability.

The rapid digital transformation across verticals such as BFSI, healthcare, retail, and manufacturing is also contributing to the expansion of the data warehousing market. In sectors like healthcare and finance, the need for secure, compliant, and high-performance data storage and analytics solutions is paramount due to the sensitive nature of the data involved. Retailers and e-commerce platforms are leveraging data warehousing to personalize customer experiences, optimize inventory management, and enhance supply chain visibility. Meanwhile, manufacturers are utilizing data warehouses to improve operational efficiency, monitor equipment performance, and drive innovation through predictive maintenance and IoT integration.

The emergence of Cloud Data Warehouse solutions is revolutionizing the way organizations manage and analyze their data. Unlike traditional data warehouses, cloud-based solutions offer unparalleled scalability and flexibility, allowing businesses to handle fluctuating data volumes with ease. This shift is not just about storage; it's about transforming data into a strategic asset. By leveraging the power of the cloud, companies can integrate diverse data sources, perform complex analytics, and derive actionable insights in real-time. This capability is crucial for businesses aiming to stay competitive in an increasingly data-driven world.

From a regional perspective, North America continues to dominate the data warehousing market, accounting for the largest share in 2024. This leadership is attributed to the presence of major technology vendors, early adoption of advanced analytics, and a strong emphasis on digital transformation among enterprises. Europe follows closely, supported by stringent data privacy regulations and increasing investments in cloud infrastructure. The Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, expanding digital economies, and government initiatives promoting smart city development and digital governance. Latin America and the Middle East & Africa are also emerging as promising markets, with organi

Natural landscapes are crucial resources for enhancing visitor experiences in ecotourism destinations. Previous research indicates that high temperatures may impact tourists’ perception of landscapes and emotions. Still, the potential value of natural landscape perception in regulating tourists’ emotions under high-temperature conditions remains unclear. In this study, we employed machine learning models such as LSTM-CNN, Hrnet, and XGBoost, combined with hotspot analysis and SHAP methods, to compare and reveal the potential impacts of natural landscape elements on tourists’ emotions under different temperature conditions. The results indicate: (1) Emotion prediction and spatial analysis reveal a significant increase in the proportion of negative emotions under high-temperature conditions, reaching 30.1%, with negative emotion hotspots concentrated in the downtown area, whereas, under non-high temperature conditions, negative emotions accounted for 14.1%, with a more uniform spatial distribution. (2) Under non-high temperature conditions, the four most influential factors on tourists’ emotions were Color complexity (0.73), Visual entropy (0.71), Greenness (0.68), and Aquatic rate (0.6). In contrast, under high-temperature conditions, the most influential factors were Greenness (0.6), Openness (0.56), Visual entropy (0.55), and Color complexity (0.55). (3) Compared to non-high temperature conditions, high temperatures enhanced the positive effects of environmental perception on emotions, with Greenness (0.94), Color complexity (0.84), and Enclosure (0.71) showing stable positive impacts. Additionally, aquatic elements under high-temperature conditions had a significant emotional regulation effect (contribution of 1.05), effectively improving the overall visitor experience. This study provides a data foundation for optimizing natural landscapes in ecotourism destinations, integrating the advantages of various machine learning methods, and proposing a framework for data collection, comparison, and evaluation of natural landscape perception under different temperature conditions. It thoroughly explores the potential of natural landscapes to enhance visitor experiences under various temperature conditions and provides sustainable planning recommendations for the sustainable conservation of natural ecosystems and ecotourism.