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1 – 10 of over 1000Fitri Rahmafitria and Regan Leonardus Kaswanto
One of the crucial elements of addressing global climate challenges through urban tourism is the continuing existence of urban forests. The reasoning is that the ecological…
Abstract
Purpose
One of the crucial elements of addressing global climate challenges through urban tourism is the continuing existence of urban forests. The reasoning is that the ecological attraction of urban forests can impact visitors’ intention to conduct pro-environmental behavior, including low-carbon actions. Thus, more visitors to urban forests will positively affect enhancing the quality of the urban environment. However, the extent to which ecological attraction can influence pro-environmental behavior warrants further investigation due to the complexity of psychosocial factors that impact behavioral intention. The main objective of this research is to examine the effects of the ecological attractiveness of urban forests on the pro-environmental behavior of visitors by exploring motivation, ecological experience, perceived value and knowledge as mediators. Moreover, whether the nature of the urban forest and facilities attract visitors simultaneously is also studied.
Design/methodology/approach
Data were collected from 615 respondents who visited three urban forests in Bandung, the second-most populous city in Indonesia, by five-point Likert questionnaires. As an analytical tool, SEM PLS was applied to establish the effect of the ecological performance of the urban forest on the increase in environmentally conscious behavior among urban forest visitors.
Findings
The findings demonstrate that the attractiveness of an urban forest affects the growth of environmentally responsible behaviors. Nonetheless, the attractiveness of urban forests is dictated more by their infrastructure than their ecological function. On the contrary, the visitors’ knowledge level can improve their motivation, environmental experience and perceived environmental value. These findings show the significance of developing educational programs with an emphasis on the experience of the visitors so that their ecological performance can contribute to improved low-carbon behavior. In conclusion, this work contributes to the management of sustainable urban tourism.
Research limitations/implications
This work also has some limitations. First, the medium R-square on intention behavior to low-carbon action suggests investigating other influential factors to produce a more robust conscious behavior. Mkono and Hughes (2020) mention that many complex factors that cause positive intention do not necessarily lead to environmental action. Thus, many psychosocial variables need to be explored in different models. Second, the convenient sampling used here does not represent the whole population, making generalization difficult. Thus, further work needs to apply more rigorous sampling techniques to validate the findings. Further investigations may also need to be conducted in other urban forests in another Asian country with a similar and different social context for benchmarking, as this study found that the type of attractive urban forest design is a more dense forest, which differs from other studies based in Europe. Exploring more influencing behavioral factors of pro-environmental action in the model is also suggested. Thus, we could contribute more to support recreational activities in urban forests.
Practical implications
As an implication for planning an urban forest to increase its recreational function, the authors illustrate the importance of producing educational programs. Although the improved knowledge of visitors has been shown to strengthen their commitment to perform pro-environmental actions, the mediating role of motivation, experience and perceived value reveals that some activities are required to achieve visitor motivation to actual behavior. Consequently, designing an urban forest requires not only the enhancement of eco-attractions and artificial elements for the convenience of visitors but also the development of an environmental education program that can improve visitors’ environmental experience and perception of ecological value. The designed educational program may use an experiential education approach incorporating objective knowledge of Earth’s current state. The urban forest education program must encourage visitors’ connection and participation with nature. Moreover, knowledge and information about Earth’s environmental quality can increase visitors’ perceived value, ensuring that their activities in the urban forest contribute to improved health, environmental quality and social environment. Thus, with well-managed and provided education, they are encouraged to adopt low-carbon action because it complements their contribution to a better quality environment.
Originality/value
The theoretical contribution of this research is generated through the role of urban forest attractiveness in the intention to conduct low-carbon action, which influences solutions to existing urban environmental problems. This work exhibits that both ecological attractiveness and attractiveness of artificial elements in urban forests can attract visitors and subsequently boost their outdoor recreation motivation, ecological experience and perceived value and then turn them to boost their intention to conduct low-carbon action. The physical characteristics of a site are behavioral stimuli that can increase a person’s motivation, experience and perception of the value of the environment, thereby increasing their intention to engage in low-carbon actions. This environment behavioral construction is fundamental in understanding that urban forests offer ecological benefits and influence the social quality of urban communities. Nevertheless, without visitor activity, urban forests are merely physical entities that become increasingly demanding to maintain. Due to this, an urban forest that is socially active and has an influence on promoting environmentally conscious behavior is needed, and its presence is becoming ever more crucial. This work shows the significance of integrating psychosocial approaches into managing tourism in urban forests.
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Nguyen Thi Dinh, Nguyen Thi Uyen Nhi, Thanh Manh Le and Thanh The Van
The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the…
Abstract
Purpose
The problem of image retrieval and image description exists in various fields. In this paper, a model of content-based image retrieval and image content extraction based on the KD-Tree structure was proposed.
Design/methodology/approach
A Random Forest structure was built to classify the objects on each image on the basis of the balanced multibranch KD-Tree structure. From that purpose, a KD-Tree structure was generated by the Random Forest to retrieve a set of similar images for an input image. A KD-Tree structure is applied to determine a relationship word at leaves to extract the relationship between objects on an input image. An input image content is described based on class names and relationships between objects.
Findings
A model of image retrieval and image content extraction was proposed based on the proposed theoretical basis; simultaneously, the experiment was built on multi-object image datasets including Microsoft COCO and Flickr with an average image retrieval precision of 0.9028 and 0.9163, respectively. The experimental results were compared with those of other works on the same image dataset to demonstrate the effectiveness of the proposed method.
Originality/value
A balanced multibranch KD-Tree structure was built to apply to relationship classification on the basis of the original KD-Tree structure. Then, KD-Tree Random Forest was built to improve the classifier performance and retrieve a set of similar images for an input image. Concurrently, the image content was described in the process of combining class names and relationships between objects.
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Population growth and urbanization pose several threats to terrestrial ecosystems, especially in forest ecological zones worldwide. This study examines the drivers of average…
Abstract
Purpose
Population growth and urbanization pose several threats to terrestrial ecosystems, especially in forest ecological zones worldwide. This study examines the drivers of average willingness to pay (WTP) to restore urban forests in a developing country.
Design/methodology/approach
It utilizes survey data of households and employs a robust Heckman two-step estimator with bootstrapping to address the research objective.
Findings
The study underscores the role of income, gender, education and perception of the health benefits of forests as the underlying determinants of restoration bids by respondents. These drivers have a positive and statistically significant effect on forest restoration. Education and gender appear to be the most effective by magnitude, followed by the perception of health benefits, then income. Attention is therefore drawn to relevant economic, sociocultural and psychological factors towards the goal of forestry to improve well-being in urban centres.
Originality/value
This paper seeks to add methodological insights to the literature on reforestation and land use changes in the Accra metropolitan area and the local population’s WTP for reforestation in this area. In principle, this is a case study informing about the values people hold for forests in Ghana and Africa, where a knowledge gap exists with respect to their socio-economic valuation.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0618
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Donna Asteria, Putri Alvernia, Berliana Nur Kholila, Sabarina Isma Husein and Farha Widya Asrofani
The Baduy tribe has its own uniqueness and values regarding the forest; it manages the forest using customary law to keep it sustainable. This research aims to describe the…
Abstract
Purpose
The Baduy tribe has its own uniqueness and values regarding the forest; it manages the forest using customary law to keep it sustainable. This research aims to describe the position of customary law used by the Baduy tribe to conserve forest areas.
Design/methodology/approach
This research is a qualitative research conducted in September 2019 and 2020 at Baduy. The data were collected through a literature study and in-depth interviews with informants related to the Baduy tribe. The collected data included documentation and interview transcripts that were translated into English. Data analysis was conducted in a descriptive manner, equipped with related evidence.
Findings
The Baduy community holds firm to its customs and culture called pikukuh. The Baduy community applies the concept of sustainable forest management in that local communities are directly involved in forest management activities to improve welfare and implement sustainable forests.
Practical implications
The implication of this research is that it is beneficial for forest conservation based on customary law, using the conservation approach of the Baduy tribe as a local community in protecting the sustainability of forest resources and their sustainability for the next generation. This study contributes as a guide for the government to formulate policies that will include local communities into conservation programs and government policies. It may apply to a study of coordination with related institutions such as the Ministry of Environment and Forestry in implementing forest conservation.
Originality/value
This study uses primary data from the Baduy tribe, which has unique local traditional values regarding the territory and the important role of the forest. The originality of the findings from the excavation of each activity was based on the procedures and beliefs regulated in customary law regarding forest management. Preservation of traditional knowledge in customary law has contributed to the urgency of sustainable forest conservation and biodiversity conservation, which is part of the traditional knowledge of the Baduy tribe.
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Background: Commodity-driven deforestation is a major driver of forest loss worldwide, and globalisation has increased the disconnect between producer and consumer countries…
Abstract
Background: Commodity-driven deforestation is a major driver of forest loss worldwide, and globalisation has increased the disconnect between producer and consumer countries. Recent due-diligence legislation aiming to improve supply chain sustainability covers major forest-risk commodities. However, the evidence base for specific commodities included within policy needs assessing to ensure effective reduction of embedded deforestation.
Methods: We conducted a rapid evidence synthesis in October 2020 using three databases; Google Scholar, Web of Science, and Scopus, to assess the literature and identify commodities with the highest deforestation risk linked to UK imports. Inclusion criteria include publication in the past 10 years and studies that didn't link commodity consumption to impacts or to the UK were excluded. The development of a review protocol was used to minimise bias and critical appraisal of underlying data and methods in studies was conducted in order to assess the uncertainties around results.
Results: From a total of 318 results, 17 studies were included in the final synthesis. These studies used various methodologies and input data, yet there is broad alignment on commodities, confirming that those included in due diligence legislation have a high deforestation risk. Soy, palm oil, and beef were identified as critical, with their production being concentrated in just a few global locations. However, there are also emerging commodities that have a high deforestation risk but are not included in legislation, such as sugar and coffee. These commodities are much less extensively studied in the literature and may warrant further research and consideration.
Conclusion: Policy recommendations in the selected studies suggests further strengthening of the UK due diligence legislation is needed. In particular, the provision of incentives for uptake of policies and wider stakeholder engagement, as well as continual review of commodities included to ensure a reduction in the UK's overseas deforestation footprint.
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Sihan Cheng and Cong Cao
Based on cognitive evaluation theory and gamification affordances, this study aims to understand how gamification affordances influence users’ intention to engage in sustainable…
Abstract
Purpose
Based on cognitive evaluation theory and gamification affordances, this study aims to understand how gamification affordances influence users’ intention to engage in sustainable behaviour and how new trends in Ant Forest influence its impact on green intrinsic motivation to support sustainable behaviours.
Design/methodology/approach
The authors developed a research model to explore the mechanisms underlying gamification affordances, psychological needs and green intrinsic motivation. Partial least squares structural equation modelling was used to assess the survey data (n = 393) and test the research model.
Findings
The results show that different gamification affordances can satisfy users’ needs for autonomy, competence and relatedness, which positively influences their green intrinsic motivation and engagement in sustainable behaviours. However, some affordances, such as competition, might negatively impact these psychological needs.
Originality/value
This research updates information system research on environmental sustainability and the Ant Forest context. The authors provide a new framework that links gamification affordances, psychological needs and sustainable behaviour. The study also examines changing trends in Ant Forest and their implications.
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As a typical nature-based solution to climate change, forestry carbon sinks are vital to achieving carbon neutrality in China. However, regulations in China are insufficient to…
Abstract
Purpose
As a typical nature-based solution to climate change, forestry carbon sinks are vital to achieving carbon neutrality in China. However, regulations in China are insufficient to promote the development of carbon offset projects in forestry. This study aims to identify the regulatory obstacles impeding the development of forestry offsets under China’s certified emission reduction (CCER) and explore ways to improve the regulatory system.
Design/methodology/approach
This study conducts a qualitative analysis using a normative legal research method. This study conducted a synthetic review of national and local regulatory documents to gain insights into the regulatory landscape of forestry offsets in China. The main contents and characteristics of these documents are illustrated. Furthermore, related secondary literature was reviewed to gain further insight into forestry offset regulations and to identify significant gaps in China’s CCER regulation.
Findings
Forestry offset regulations under the CCER are characterized by fragmentation and a relatively lower legally binding force. There is no systematic institutional arrangement for forestry offset development, impeding market expectations and increasing transaction costs. The main challenges in China’s regulation of forestry carbon sinks include entitlement ambiguity, complicated rules for registration and verification, a lack of mechanisms for incentives, risk prevention and biodiversity protection.
Originality/value
Forestry carbon sinks’ multiple environmental and social values necessitate their effective development and utilization. This study assessed forestry offset regulations in China and proposed corresponding institutional arrangements to improve forestry carbon sink regulations under the CCER.
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Cheng Liu, Yi Shi, Wenjing Xie and Xinzhong Bao
This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.
Abstract
Purpose
This paper aims to provide a complete analysis framework and prediction method for the construction of the patent securitization (PS) basic asset pool.
Design/methodology/approach
This paper proposes an integrated classification method based on genetic algorithm and random forest algorithm. First, comprehensively consider the patent value evaluation model and SME credit evaluation model, determine 17 indicators to measure the patent value and SME credit; Secondly, establish the classification label of high-quality basic assets; Then, genetic algorithm and random forest model are used to predict and screen high-quality basic assets; Finally, the performance of the model is evaluated.
Findings
The machine learning model proposed in this study is mainly used to solve the screening problem of high-quality patents that constitute the underlying asset pool of PS. The empirical research shows that the integrated classification method based on genetic algorithm and random forest has good performance and prediction accuracy, and is superior to the single method that constitutes it.
Originality/value
The main contributions of the article are twofold: firstly, the machine learning model proposed in this article determines the standards for high-quality basic assets; Secondly, this article addresses the screening issue of basic assets in PS.
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Anupam Saxena, Sugandha Shanker, Deepa Sethi, Manisha Seth and Anurag Saxena
This study was conducted to analyse the socio-ecological problems faced by the Suhelwa Wildlife Sanctuary and understand its potential and challenges for developing ecotourism…
Abstract
Purpose
This study was conducted to analyse the socio-ecological problems faced by the Suhelwa Wildlife Sanctuary and understand its potential and challenges for developing ecotourism following Triple Bottom Line (TBL) principles. The study also benchmarked best ecotourism practices across the globe to create an ecotourism plan that would provide alternative livelihood and help in sustainable management of the area by reducing poverty, dependency on forests and biodiversity protection.
Design/methodology/approach
Suhelwa Wildlife Sanctuary was chosen because this area has several socio-ecological crises with limited livelihood options, and there is an urgent need for alternative livelihood opportunities in the form of ecotourism. The study followed an ethnographic approach through observation, participant observation, and semi-structured interviews. Content and thematic analysis was conducted through Atlas Ti9.0 software for data analysis. Subsequently, benchmarking best ecotourism practices through a literature review was done to develop an ecotourism action plan.
Findings
The First finding was related to the study area divided into three themes: problems, potential for ecotourism development, and challenges for ecotourism development. The second finding was related to benchmarking best practices and suggesting an action plan.
Originality/value
This work studied an area not sufficiently acknowledged by academicians and policymakers concerning ecotourism development. The work also benchmarks the best practices for ecotourism and proposes a sight-specific ecotourism action plan in accordance with TBL.
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Marcelo Cajias and Anna Freudenreich
This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.
Abstract
Purpose
This is the first article to apply a machine learning approach to the analysis of time on market on real estate markets.
Design/methodology/approach
The random survival forest approach is introduced to the real estate market. The most important predictors of time on market are revealed and it is analyzed how the survival probability of residential rental apartments responds to these major characteristics.
Findings
Results show that price, living area, construction year, year of listing and the distances to the next hairdresser, bakery and city center have the greatest impact on the marketing time of residential apartments. The time on market for an apartment in Munich is lowest at a price of 750 € per month, an area of 60 m2, built in 1985 and is in a range of 200–400 meters from the important amenities.
Practical implications
The findings might be interesting for private and institutional investors to derive real estate investment decisions and implications for portfolio management strategies and ultimately to minimize cash-flow failure.
Originality/value
Although machine learning algorithms have been applied frequently on the real estate market for the analysis of prices, its application for examining time on market is completely novel. This is the first paper to apply a machine learning approach to survival analysis on the real estate market.
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