Residents’ Willingness to Pay for a Carbon Tax

Addressing environmental issues has been a significant challenge. Malaysia is one of the fastest-growing countries in terms of economic, social, and land use development but high in CO2 emission rates. The introduction of a carbon tax is seen to reduce greenhouse gases emission (GHG), but the uncertain extent of implementation, based on economic theory, remains unknown. Hence, the current study’s objectives are to assess residents’ knowledge and attitude towards GHG. It is also to analyse the factors influencing residents’ Willingness to Pay (WTP). Three hundred and eleven (311) residents from Klang were selected using convenience sampling. The result shows that most of the respondents were willing to pay and had medium knowledge and a high level of attitude towards GHG. Poisson regression analysis results showed that gender, age, income, education, number of households, and marital status variables significantly influenced the maximum WTP. Overall, the residents’ WTP for a carbon tax was estimated at RM36.31 per year for open-ended (CVM): RM36.96 and double bound (CVM): RM35.65. A mechanism such as investment in green technology, eco-transportation, and green energy using the tax can be applied. This study is pivotal towards achieving SDG 13: Climate action.

The Imprint of Built-Up Land Expansion on Cropland Distribution and Productivity in Shandong Province

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

A COMPARATIVE ANALYSIS OF TRANSIT ORIENTED DEVELOPMENT GUIDELINES BETWEEN MALAYSIA AND OTHER COUNTRIES

Transit Oriented Development (TOD) is an emerging concept to optimise the land use development surrounding a transit station and to create a reliable relationship between a densely compact urban form and high public transportation ridership. In Malaysia, the concept of TOD was initiated in the first National Physical Plan in 2005 and mentioned again in National Physical Plan 2 in 2010. This paper identifies the principle of TOD applied in Malaysia and other countries, and discusses the differences and similarities of the TOD practices using comparative analysis and document analysis methods. Findings of this study indicate that mixed-use development, high density, intensity and connectivity are the main TOD principles adopted in Malaysia and other countries around the globe. The current policy and practises of these countries tend to focus more on the better management and increase ridership of these transit rail stations, including ways to encourage users to shift from private vehicles to public transportation. The findings of this study would contribute towards policy decisions and practices of TOD in Malaysia.

Modeling Future Land Use Development: A Lithuanian Case

Effective management decisions regarding greenhouse gas (GHG) emissions may be hampered by the lack of scientific tools for modeling future land use change. This study addresses methodological principles for land use development scenario modeling assumed for use in processes of GHG accounting and management. Associated land use policy implications in Lithuania are also discussed. Data on land uses, available from the National Forest Inventory (NFI) and collected for GHG accounting from the land use, land use change and forestry (LULUCF) sector in the country, as well as freely available geographic information, were tested as an input for modeling land use development in the country. The modeling was implemented using the TerrSet Land Change Modeler. Calibration of the modeling approach using historical land use data indicated that land use types important for GHG management in the LULUCF sector were predicted with an accuracy above 80% during a five-year period into the future, while the prediction accuracy for forest and built-up land was 96% or more. Based on several land management scenarios tested, it was predicted that the LULUCF sector in Lithuania will accumulate CO2, with the forest land use type contributing most to CO2 absorption. Key measures to improve the GHG balance and carbon stock changes were suggested to be the afforestation of abandoned or unused agricultural land and prevention of the conversion of grassland into producing land.

Using Deep Machine Learning to Understand Green Space Popularity, Activities, Functionality, and Design Implications: a comparison of Chicago and Stockholm

<p>Green spaces have a significant effect on urban living environments, providing shared natural areas and entertainment spaces among other benefits. Investigating their popularity and functionality is valuable for supporting green space design and guiding land use development around public green spaces. Conventional methods used to extract use and functionality information on public green spaces have typically relied on questionnaires and in-site observations, both resource and time consuming processes. These approaches can also be context dependent and produce less transferable data across regions. This study utilizes deep machine learning techniques to mine social media text and image data to produce useful information on public green space popularity, activities, functionality and design.  Convolutional neural networks (CNN), an advanced machine learning technique, is used to analyze large scale data sets (Yelp and publicly accessible images) of public green spaces in Chicago, Illinois, US. The coupling of the two types of data enables the extraction of a rich and comprehensive analytical frame for understanding how green spaces are used and how they might be improved. The technique also utilizes a complex transfer learning process to pretrain the model and allow its quick adaptation to other regions around the world. The process will be replicated in Stockholm, Sweden. We use the analysis generated to compare open spaces in the 2 cities. The process has the potential to substantially improve nature based strategies on green space development and design.</p>

Projecting future streamflow under changing climate and urban land cover across the UK

<p>Changes in precipitation and land cover are important drivers of change in catchment streamflow, yet quantifying their influence remains a major challenge. This work aims to understand how streamflow may evolve under different scenarios of future precipitation and urbanization across the UK. A collection of catchments from the National River Flow Archive (NRFA) that have experienced significant changes in flows and urbanization were selected. Both historical observations and future projections of precipitation and urban land cover were extracted within each study catchment, for different emissions and socio-economic scenarios including Representative Concentration Pathways (RCPs) and Shared Socio-Economic Pathways (SSPs). Distributional regression models – Generalised Additive Models for Location Scale and Shape (GAMLSS) – were developed using historical precipitation, land cover, and streamflow, and employed to project future streamflow using bias-corrected projections of precipitation and land cover. The results improve our understanding of streamflow response to climate and land cover changes and provide further insights for water resources management and land use development.</p>

Multiple Dimensions of Smart Cities’ Infrastructure: A Review

In recent years, there has been significant focus on smart cities, on how they operate and develop, and on their technical and social challenges. The importance of infrastructure as a major pillar of support in cities, in addition to the rapid developments in smart city research, necessitate an up-to-date review of smart cities’ infrastructure issues and challenges. Traditionally, a majority of studies have focused on traffic control and management, transport network design, smart grid initiatives, IoT (Internet of Things) integration, big data, land use development, and how urbanization processes impact land use in the long run. The work presented herein proposes a novel review framework that analyzes how smart city infrastructure is related to the urbanization process while presenting developments in IoT sensor networks, big data analysis of the generated information, and green construction. A classification framework was proposed to give insights on new initiatives regarding smart city infrastructure through answering the following questions: (i) What are the various dimensions on which smart city infrastructure research focuses? (ii) What are the themes and classes associated with these dimensions? (iii) What are the main shortcomings in current approaches, and what would be a good research agenda for the future? A bibliometric analysis was conducted, presenting cluster maps that can be used to understand different research trends and refine further searches. A bibliographic analysis was then followed, presenting a review of the most relevant studies over the last five years. The method proposed serves to stress where future research into understanding smart systems, their implementation and functionality would be best directed. This research concluded that future research on the topic should conceptualize smart cities as an emergent socio-techno phenomenon.

Modeling Urban Futures: Data-Driven Scenarios of Climate Change and Vulnerability in Cities

Cities are complex systems where social, ecological, and technological processes are deeply coupled. This coupling complicates urban planning and land use development, as changing one facet of the urban fabric will likely impact the others. As cities grapple with climate change, there is a growing need to envision urban futures that not only address more frequent and intense severe weather events but also improve day-to-day livability. Here we examine climate risks as functions of the local land use with numerical models. These models leverage a wide array of data sources, from satellite imagery to tax assessments and land cover. We then present a machine-learning cellular automata approach to combine historical land use change with local coproduced urban future scenarios. The cellular automata model uses historical and ancillary data like existing road systems and natural features to develop a set of probabilistic land use change rules, which are then modified according to stakeholder priorities. The resulting land use scenarios are evaluated against historical flood hazards, showcasing how they perform against stakeholder expectations. Our work shows that coproduced scenarios, when grounded with historical and emerging data, can provide paths that increase resilience to weather hazards as well as enhancing ecosystem services provided to citizens.

A Review on the Significance of Integrated Transportation and Land Use Planning Model for the Planning of Urban Areas

Planning for transportation infrastructure takes significant role in development of urban areas. Proper planning is needed for eliminating the problems like overcrowding, housing shortage, congestion etc. So there is a need of integrating transportation and land use. Transportation planning and the land use planning have to be done together. Integrating transportation with land use helps to decrease travel length and need to travel. Mixed land use development is more suitable for the urban areas. This paper critically reviews the importance of Integrated Transportation and Land Use planning (ITLUP) model in the planning of urban areas and applying this model as a solution for most of the problems facing in urban areas by analysing the best practices. The review also focuses on the relationship between land use and transportation by examining the parameters of ITLUP model.

SMOOTHED GRIDDED SEISMICITY EFFECT FOR LAND-USE DEVELOPMENT, CASE STUDY: KALIMANTAN ISLAND, INDONESIA

Smoothed gridded seismicity is an analysis model in seismicity that allows for the obtaining of a rate based on the b-value and the same magnitude range. The data study has been collected and analyzed from a background source gathered by PusGen, referred to as the PusGen catalogue, with approximately 70 thousand data points. Two software programs (i.e. USGS PSHA and OpenQuake) were utilized in this study, and both programs have been proven as reliable in the creation of the 2017 Indonesia Earthquake Hazard Map. The final steps are to compare the acceleration map results with the Kalimantan Island land-use map and to analyze suitability development planning against potential hazards and earthquake risks. The results stated that: (1) acceleration due to the shallow background earthquake for the Kalimantan region, range from 0.00-0.25g (using USGS PSHA) and 0.0-0.4g (using OpenQuake); (2) meanwhile, based on the deep background earthquake source, the maximum accelerations that occur are 0.15g and 0.25g when using USGS PSHA and OpenQuake, respectively; (3) the utilized of land-use for the current and future years is in line with the results of the acceleration simulation. The study recommends to take into account the seismic aspects in new planning of the capital city, mining and residential areas in order to reduce the existing risks.