Search Results (29)

The ageing population and rising housing costs in Switzerland are increasing the number of older adults facing financial housing concerns. Older women have particularly limited housing choices because they, on average, earn less, live longer, and are more likely to live alone. This study explores potential levers to alleviate housing-related financial fears among baby boomer women (aged 55–75) living alone in Switzerland, a subject with limited academic coverage. Using regression and random forest models on unique 2023 survey data ($N=371$), we examine the influence of socio-demographic, financial, well-being, and housing factors on fears related to affordability, price increases, and lack of housing supply. Key findings show that ownership status, perceived financial situation, and concerns about maintaining one’s lifestyle significantly drive these fears. The fear of unsuitable housing strongly influences perceived lack of supply. These results highlight the importance of retirement planning and support the consideration of measures such as reverse mortgages, co-housing, subsidies, and rent-controlled units. Full article

This study analyzed citizens’ subjective perceptions and attitudes toward housing development in Norwalk, Connecticut, employing structural equation modeling (SEM) and machine learning techniques. A survey of 580 residents was conducted to study the relationship between the willingness to buy or rent from housing projects in Norwalk and perceptions of risks, benefits, and sustainability beliefs. SEM identified latent variables influencing attitudes, including perceived risks (e.g., overcrowding and the loss of historical character), benefits (e.g., enhanced infrastructure, and job creation), and sustainability beliefs. The results revealed that perceived benefits have a stronger positive impact on citizens’ attitudes than risks, mediated by sustainability beliefs in this city. Machine learning via random forest highlighted key predictors of willingness to buy or rent, such as affordability, walkability, and public infrastructure improvements, with affordability being the most influential factor. These findings emphasize the importance of framing housing projects to align with citizens’ priorities, especially through campaigns emphasizing economic and environmental benefits. The integration of SEM and machine learning offers a robust approach to understanding citizen preferences, ensuring greater support and citizens’ acceptance for sustainable housing initiatives. These findings provide actionable insights for urban planners to understand the interrelations between concerns, perceived benefits, and beliefs to foster the social acceptance of sustainable urban policies. Full article

Forests serve as the lungs of our planet, yet their mismanagement causes environmental problems and threatens global sustainability. Global forest footprints continue to increase, requiring studies to investigate and provide solutions. This study aims to establish how forest rents and government effectiveness shape forest footprints in China. Specifically, it assesses the impact of forest rents (FRs), fossil fuel consumption (FFC), foreign direct investment (FDI), economic growth (GDP), population (POP), and ecological footprints (EFFs) while considering the moderating role of government effectiveness (GEFF). This study used quantile regression, ordinary least squares, and Granger causality tests for a comparative analysis. This study found that forest rents significantly increase ecological footprints, but the impact diminishes at higher quantities, an indication that environmental policies can mitigate their adverse effects. Moreover, GEFF plays a crucial role in reducing EFFs across all quantiles, signifying the relevance of effective governance in achieving sustainability. Again, while FFC and FDI contribute to environmental sustainability, economic growth exacerbates ecological degradation, particularly at higher quantiles. The Granger causality test further indicates that forest rents and government effectiveness drive ecological changes, while population growth exerts a bidirectional influence on sustainability. These findings provide critical insights for policymakers and emphasize the need for robust governance, sustainable forest management, and eco-friendly economic strategies. Full article

In the process of forest ecological benefit compensation, there are problems of information asymmetry and “misaligned incentives”, which will reduce the compensation efficiency. In order to improve the compensation efficiency, based on principal–agent theory, this study constructs incentive contract models for governmental forest ecological benefit compensation under three different scenarios, namely, information symmetry, single-sided information asymmetry, and double-sided information asymmetry. The study finds that the government can design different incentive contracts to motivate forest farmers with high and low forestry capabilities. And the government’s expected utility is influenced by the proportion of forest farmers with high and low forestry capabilities in reality. Due to the information gap between the government and forest farmers, it is inevitable that high-capability forest farmers will obtain an information rent. Under double-sided information asymmetry, the incentive coefficient for lower-capability forest farmers and their optimal actual public welfare forest conservation area decrease as the proportion of high-capability forest farmers increases. Furthermore, when the proportion of high-capability forest farmers exceeds a certain threshold, signing compensation contracts with low-capability forest farmers can harm the government’s interests. The research conclusions provide a scientific basis for the government to formulate differentiated incentive contracts for forest ecological benefits. This can effectively align forest farmers’ conservation behaviors with the improvement of public forest ecological benefits. As a result, it contributes to improving the efficiency of forest ecological benefit compensation. Full article

In China, approximately 20% of the permanent population are renters, with 91% of leased land concentrated in first-tier and new first-tier cities. Education and healthcare are primary concerns for residents, significantly influencing rental decisions due to the household registration (hukou) system, competitive educational environment, and uneven distribution of medical resources. This study explores the distinct factors affecting rental decisions in China’s super cities, differing from other countries where renters prioritize proximity to work or urban amenities. Using advanced interpretable machine learning techniques, the study analyses rental markets in Beijing, Shanghai, and Shenzhen. The random forest model demonstrates superior performance in rent prediction across all three cities. The results indicate that the impact of public service resources on rent is notably higher in Beijing and Shanghai, while in Shenzhen, balanced urban planning results in property characteristics being more prominent in tenant preferences. These findings enhance the understanding of global rental market dynamics and provide recommendations for promoting sustainable rental housing development. The scientific novelty of this study lies in its application of advanced machine learning models to identify and quantify the unique influences of public service resources on rental markets in different urban contexts. Full article

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers. Full article

Grassland ecosystems are increasingly vulnerable as they are threatened by both intensive agriculture and abandonment of land use, which leads to overgrowth with scrub vegetation and forest. Given that meadows are habitat types of very high biodiversity, their loss significantly reduces local biodiversity. That is why the University Botanic Gardens Ljubljana has been renting a 2 ha dry meadow at the edge of Ljubljana capital city since 2001, for the purpose of in situ conservation in the urban area. We have been observing the meadow since 1997. In 2023, in addition to the complete inventory of species, we also carried out an inventory and analysis of the community in the meadow using the Braun–Blanquet method in 25 relevés. We recorded 163 plant species in the meadow during the entire growing season, and a total of 82 were recorded in the relevés. Eighteen of the species recorded are on the red list of protected plant species in Slovenia. In the relevés, 15 species types were constant (occurring in 60%) and as many as 21 were unique. The species Peucedanum oreoselinum (L.) Moench was recorded in all relevés (25), followed by the species Bromopsis erecta (Huds.) Fourr. (24), Galium verum L. (24), Briza media L. (23), Brachypodium pinnatum (L.) PB. (22) and Salvia pratensis L. (21). Full article

This study examines the relationship between entrepreneurship density and environmental quality in 28 Asia-Pacific countries using the PMG estimator as a panel data estimation method in the context of the ARDL model. The study finds that entrepreneurship density has no statistically significant short-term effects on CO₂ emissions in all three economic sectors, but it appears to have statistically significant effects on CO₂ emissions in agriculture and industry in the long run. The study suggests that the nature of entrepreneurship activities and their impact on the environment changes from low-income to high-income countries, with entrepreneurship activities with innovations and creativity primarily found in the industrial sector, improving economic efficiency and reducing industrial emissions. However, entrepreneurship activities with natural resource rents, such as large land use or forest rents, can cause environmental degradation. The study provides further insights by interacting entrepreneurship density with the income variable, revealing that entrepreneurship density has different effects on sectoral emissions in low, lower-middle, upper-middle, and high-income countries. Finally, the study provides interesting findings on the relationship between entrepreneurship density and environmental quality, such as biodiversity and water quality. Full article

This research tried to identify the most important factors that motivate or hinder forest engineers in commuting between their household and the regional office of the National Forest Administration, or prompt them to relocate their entire family to Bucharest, where the headquarters of the NFA is located, in seeking promotion in the professional hierarchy. A survey, administered as a Google form, was designed to carry out analysis of independent benefits, opportunities, costs, and risks. The decision process was designed as an analytic hierarchy process, and each one of these factors was a separate analytic hierarchy process. We found that forest engineers tend to be very conservative when it comes to changing their professional position from the forest district to the NFA regional office or NFA top management. On the one hand, the professional opportunities to promote upwards do not compensate for the fringe benefits gained at the forest district level, while the costs of living in a larger city outweigh the benefits, even though the house rent is paid by the employer. Full article

The Faustmann formula, equivalent to the land expectation value (LEV), yields the present value, starting with bare land, of an infinite series of future timber rotations for a stand. If this formula is used to find the rotation age that maximizes the present value, a lower annual harvest will result when compared to a maximum sustained yield (MSY) regime for an ownership with many stands. However, the LEV is strongly preferred by economists. This is the LEV vs. rents paradox. Herein, this paper demonstrates that an infinite series of annual harvests for an ownership under an even flow regime, discounted to the present using any interest rate, will provide the same optimal rotation age as the time of the peak MAI for a single stand, though with different estimates for the profitability, depending on the interest rate. Thus, rotations producing the MSY and the maximum financial return are identical when analyzed at the ownership scale but are not the same when based on the analysis at the stand scale using the Faustmann formula (LEV). This is the solution to the paradox. The formulas for internal rate of return (IRR) and the land valuation for transaction purposes are also presented. Full article

With population ageing being a notable demographic phenomenon, aging in place is an efficient model to accommodate the mounting aging needs. Based on the community scale, this study takes the 15-min community-life circle as the basic research unit to investigate the imbalanced distribution of pension resources and its influencing factors in downtown Shanghai. We obtained six types of elderly care facilities data from the Shanghai elderly care service platform and utilized the Gaussian 2-step Floating Catchment Area method to calculate the accessibility of 6-type elderly care facilities. Then, we used the Entropy Weight Method to calculate the comprehensive accessibility of elderly care facilities. The Getis–Ord Gi* method was adopted to analyze the overall distribution, identifying the well-developed and the under-developed areas. To explore the influencing factors of the distribution, this paper obtained multi-source data to construct a total of 17 indicators and established a Random Forest model to identify the feature importance. With the selected eight factors, the Geographically Weighted Regression (GWR) model was applied to study the spatial heterogeneity of influencing factors, and the model showed a good performance with the AdjR² being 0.8364. The findings of this research reveal the following: (1) The distribution of six types of elderly care facilities is extremely uneven, with obvious spatial aggregation characteristics. Amongst the seven administrative regions, Huangpu District has the best accessibility to pension resources, while the resources in the other six regions are highly inadequate. (2) Essential influencing factors of the comprehensive accessibility of community-based elderly care facilities are accessibility of nursing institutions (positive), hotel density (positive), catering density (negative), education density (positive) and medical density (negative), while “rents”, “plot ratio” and “building density” have little impact on comprehensive accessibility. (3) The results of GWR revealed that the eight indicators are heterogeneous in space, all of which have bidirectional effects on comprehensive accessibility. By investigating the spatial distribution patterns and influencing factors of pension resources in Shanghai, this research could further contribute to establishing a sound community-based elderly care service system that improves older adults’ quality of life and promotes social fairness and justice. Full article

Preservation and promotion of agroforestry systems entails the ideology for more ecosystem services, additional biodiversity benefits and climate change mitigation. Furthermore, farmland and forest landscapes and the consequent benefits to the environment from their combination, enhance the importance of agroforestry systems towards sustainable environmental policies. Nevertheless, traditional agroforestry systems face significant adaptation problems, especially in the EU, due to continuous economic reforms and strict agri-environmental measures. In this context our main goal is to assess the current managerial framework of two agroforestry systems and more specifically the olive agroforestry practices in Northern Greece. The economic and environmental implications of four different production plans are highlighted following the Life Cycle Costing and the Life Cycle Assessment protocols. The production plans include the simultaneous cultivation of annual crops, such as vetch and barley, along with olive groves. Potential environmental impacts are depicted in CO₂ equivalents, while the economic allocation of costs is divided in targeted categories (e.g., raw materials, labor, land rent, etc.). The results indicate significant deviations among the four production plans, with the combination of olive trees and barley being heavily dependent on fertilization. Furthermore, the open-spaced olive trees intercropped with a mixture of barley and commonly depicted the lowest CO₂ eq. emissions, though the economic cost was significantly higher than the other agroforestry system intercropped with barley only. The authors suggest that the formulation of a decision support system for agroforestry systems should be taken into account in order to preserve current agroforestry systems. Full article

Economic globalization (EG) accelerates very fast in Central Asia. This could cause environmental degradation, according to the environmental Kuznets curve (EKC) hypothesis. The study aims to determine how the EG of agriculture impacts environmental sustainability, and to test the EKC hypothesis on the agricultural sector in six Central Asian countries. Particularly, some main hypotheses were proposed using secondary data from Kazakhstan, Kyrgyzstan, Mongolia, Tajikistan, Turkmenistan, and Uzbekistan from 1994 to 2019. This study uses five explanatory variables: agricultural exports value (EXP), agriculture forestry and fishing value-added (AVA), the exchange rate (EXR), total natural resource rents (RENT), and external debt stocks (DEBT), while the dependent variable in this study is the CO₂ emissions from on-farm energy use (EMS), temperature changes (TEMP), and forest fires (FIRE). These data are analyzed using panel data regression. As a result, AVA and RENT raise EMS; EXC raises TEMP but lowers EMS; DEBT raises TEMP but can lower FIRE. Hence, we propose recommendations to improve this condition, including a clear roadmap, enhanced partnerships, and regional and international support. Full article

In this paper, an attempt is made to determine an intangible capitalization premium based on an expected further value increment of forest stands. Such premium cannot be determined through exponential interpolation. Firstly, any discount rate depending on maturity proposes clearcuttings soon after thinning as a computational artifact. Secondly, exponential interpolation with a constant discount rate violates an internal consistency criterion as the rotation age increases. Omitting the intangible capitalization premium, the carbon stock of boreal forest can be increased in a variety of ways (albeit at the expense of a capital return rate deficiency). A small excess volume can be economically gained by increasing sapling density. Greater excess volume is best achieved by restricting thinnings. A large excess volume is best achieved by omitting thinnings. Regardless of the technique used, enhanced carbon storage requires financial compensation in terms of a carbon rent. With the present European emission prices, there is no financial difficulty in establishing such a carbon rent arrangement. Full article

Forest management at the landscape level is a requirement for reducing wildfire hazard. In contexts where non-industrial private forest ownership prevails, the collaboration among multiple owners has been proposed as the way forward to reach consistent fuel management at that level. The current literature has been focused on identifying the factors that lead to collaboration among owners. In this study we explored other ways to reach landscape-level management in addition to the collaborative way, such as those that may be promoted through land renting or selling. Different contexts and owner types may require different solutions. Thus, we explicitly asked which alternative would be chosen by a given forest owner, from the following set: keeping individual management, entering a multi-owner collaborative arrangement where they delegate management, renting to a pulp company; or selling the land. In a context of small-scale ownership and high recurrence of wildfires in Portugal, a face-to-face survey was carried out to a sample of landowners. Our results suggest that there is not an a priori generalized unwillingness of owners to delegate management, rent or sell the land and thus they seem prone to align themselves with policy strategies to promote management at the landscape level. Multinomial logit regression modelling allowed us to explain and predict owners’ choices among the aforementioned set of alternative management options. We found that choosing multi-ownership collaboration, as opposed to keeping current individual management, is associated with passive management under harsher conditions, by non-residents without bonding capital. The identified factors of owners’ choices show the limited scope of tenancy and land-market mechanisms to promote landscape-level management. The best policy option was found to depend on the owner profiles prevailing in the target area. This suggests that studying the existing context and owner types is required to design effective policies. Full article