Therefore, in addition to improving the description of the CO 2 emissions, Eq. The interconnected role of population and area on CO 2 emissions is better visualized in the contour plot of Eq. In comparison with Fig. This behavior contrasts with the equally spaced isoquants produced by the model of Eq.
In terms of density, we can rewrite Eq. Thus, the more widespread a city is, the larger is the impact of a proportionate change of population and density that is, area remains constant on its emissions.
Therefore, our estimates indicate that only decreasing returns to scale are possible when the CO 2 emissions are described in terms of population and density. The translog model of Eq. The marginal product of population represents the response in emissions associated with changes in population when density remains constant, whereas the marginal product of density expresses the response in emissions caused by a change in density when population remains constant.
We have reached the same conclusion with the simpler model of Eq. However, and as we have verified, the translog approach further refines the description of CO 2 emissions and indicates that the impact of population and density on the emissions changes with the population and density of the cities. This behavior contrasts with results of Eq.
By carrying this predictions forward and in line with other studies 33 , our results suggest that the densification of large populated urban areas is likely to have important contributions to the reduction of urban CO 2 emissions. We have shown that two conventional approaches used to study the effect of urbanization on urban CO 2 emissions suffer from confounding effects, and are unable to describe the interconnected role of population and area on urban emissions.
Inspired by the economic theory of production functions, we have proposed new models for describing urban emissions simultaneously in terms of the population and area or population density of urban units. These models not only account for such confounding factors but significantly refine the description of the emissions in terms of urban quantities.
In addition to being better fits to data, our models reveal intriguing aspects about the interplay between population and area or density on urban emissions that would be entirely neglected under the urban scaling or the per capita density scaling frameworks. When described in terms of population and density, we have found that urban emissions display decreasing returns to scale, meaning that doubling population and density of a city always associates with less than doubling its emissions.
We have further verified that changing the population of a city has more impact on its emissions than changing its density. Our work has, however, its limitations in a sense that ideally the comparison between the effects of population and area or density on the emissions should be made after accounting for every other factor such as economic activity, technology, and even individual attitudes that possibly affects urban emissions. Thus, while our models account for the confounding effects of area and density , the emissions may also be affected by other confounding factors not available in our dataset.
One possibility for addressing this problem would be to include further control variables in our models, an approach that somehow resembles the IPAT equations 34 , 35 , 36 , a framework proposed to model environmental impact I as the product of population P , affluence A , and technology T , but with the advantage of considering population density or area as a predictor and allowing the interactions among such factors. Another possibility for overcoming these possible confounding effects is to combine our approach with the recently proposed urban Kaya scaling 20 that relates CO 2 emissions, population, gross domestic product, and energy consumption.
Combining these different approaches into a single and coherent framework could represent an exciting perspective for solving the economics of urban CO 2 emissions and defining its most important covariates. However, such endeavors require homogeneous and consistent data, which are still scarce on large spatial scales.
While moving from urban units defined in terms of connected urban spaces to some political or administrative divisions would be a possibility, this approach is likely to introduce serious bias to the empirical estimates 37 in addition to overestimating urban areas 38 see Methods.
Another important limitation of our study is related to the intra-city processes and urban characteristics that cannot be accounted for only by population and area or density. Case studies on this subject have shown that the urban form and intra-city population distribution have a substantial impact on urban emissions, particularly on transportation emissions. Cities from rapid developing countries such as China and India have undergone through a remarkable decentralization and suburban growth processes These more dispersed urban forms and the consequent increase of the population living in urban frontier areas have direct implications for commuting and contribute to increasing CO 2 emissions 39 , 40 , Regarding this aspect, it would be very interesting for future works to include possible covariates able to account for population imbalance and urban form in our models and thus quantify their impact in a large scale study.
Despite of these limitations, our work adds to the current understanding about the role of urbanization on CO 2 emissions, shedding light particularly upon the role of population and urban area including their interactions on urban emissions. Such interactions are completely overlooked within the urban scaling and per capita density scaling approaches and our work demonstrates that they play an important role in the description of urban emissions. Finally, our framework can be directly applied to other urban metrics in the place of emissions, opening thus a considerable range of possibilities for investigating the interplay between population and area or population and density over other important urban metrics.
Our dataset is the same as analyzed by Gudipudi et al. As described by Gudipudi et al. Next, sectoral emissions data building and transportation are obtained from the Vulcan Project This process consists of equally splitting the emissions located in a cell of the Vulcan project among all overlapping population cells classified as urban. Finally, the city clustering algorithm CCA 27 is used to systematically define the urban units, leading to the population size P in raw counts , area A in square kilometers , and CO 2 emissions C in tonnes of CO 2 for each urban unit.
CCA is an iterative clustering algorithm that assigns any two cells to the same cluster if their distance is smaller or equal than a predefined threshold distance l. In particular, the translog model Eq. These changes affect our point estimates for the Cobb—Douglas Supplementary Fig. This dependence on l is smaller in the translog than in the Cobb—Douglas model.
We have further verified that the contour plots of the translog function Eq. These robustness tests are important because there has been a great debate about how to accurately define the correct boundaries of a city In spite of that, there is still no consensus on this issue nor has a fail-safe procedure for defining the correct boundaries of a city been proposed yet.
This issue also has great similarity with the more general concept of clustering and a quite similar issue arises when applying community detection algorithms in complex networks.
All these topics have been exhaustively studied, but no silver bullet method exists. In the case of cities, additional complexity emerges because some urban indicators are more spatially constrained than others, and also because people commute to work and move from place to place in the long run. Partly because of the seminal works by Bettencourt et al.
These definitions are based on the idea of integrated socio-economic units and appear to be the gold standard for the urban scaling hypothesis as well as other purposes. In particular, MSAs are defined by a core county or even more than one having at least 50, people aggregated with adjacent counties that display a high degree of interaction social and economic with the central county as measured by commuting flows. While this definition may work well for studying urban scaling, it is very problematic in our case.
Since MSAs are made up of counties, they often include vast rural areas which in turn hugely overestimate the urban extent areas. In addition to that, MSAs can also fragment urban clusters into different pieces.
These problems are the main reason why we have chosen the CCA to define the urban units in our study. It is worth noticing that the CO 2 emissions we have analyzed are from building and transportation, and thus primarily associated with settlements where people reside and commute. Despite these problems and limitations, we have also applied our models to emissions data associated with MSAs.
To do so, we have used the dataset provided by Fragkias et al. Supplementary Figure 12 shows the urban and per capita density scaling laws for MSAs. We notice that the quality of these relationships is not comparable with those reported in Fig. Supplementary Fig. As discussed in more detail by Bettencourt et al. We have also applied the models of Eqs. The approximate constant returns to scale observed for the functional city definitions is also likely to be related to disaggregation and aggregation effects that we previously discussed.
Finally, it is worth remarking that the CCA is also likely to suffer from disaggregation or aggregation effects, as is the case of administrative or functional city definitions. However, differently from such ad hoc definitions, CCA allows us to quantify the impact of such effects by changing the threshold distance l and to verify that our conclusions are robust under different values of l.
To relate the Cobb—Douglas model Eq. To obtain this connection, we rewrite Eq. Next, we divide both sides by A. As we have argued, our approach is inspired by the economic theory of production functions By following this analogy, we have considered the urban emissions as the output and population and area or density as the inputs of a two-factor production process mediated by cities.
In what follows, we summarize concepts from the economic theory of production functions that have been used in our work. Elasticity of scale. This measure quantifies the impact of changing population and area on emissions. Technical rate of substitution. The technical rate of substitution measures the rate at which an input must change in response to a change in the other input so that the output remains constant.
In absolute value, it represents the slope of the isoquants of the production function. Elasticity of substitution. It is defined as the ratio between a proportionate change in the inputs and the associated proportionate change in the slope of the isoquant. This measure quantifies the efficiency at which population and area substitute each other.
Marginal products. The marginal product of an input is defined as the infinitesimal change in the output resulting from a infinitesimal change in one of the inputs.
We summarize all these properties calculated for Cobb—Douglas Eq. As we have discussed in the main text, multicollinearity is present in the models of Eqs. This effect happens when at least two predictors in a multiple linear regression are correlated to each other 29 , Under this situation and depending on the degree of correlation among the predictors, ordinary-least-squares estimates of the parameters can be unstable against minor changes in the input data and also display large standard errors.
To better illustrate this problem, consider the simple linear model. If the values of predictors are strongly correlated, the inversion of the matrix X T X can become unstable, and consequently lead to unstable estimates for the linear coefficients.
To account for the multicollinearity problem, we have fitted Eqs. This is a common practice when dealing with regularization methods and ensures that the penalty term is uniformly applied to the predictors, that is, the normalization makes the scale of the predictors comparable and prevents variables with distinct ranges from having uneven penalization. The standardized version of Eq. By following this approach, we find that. Moreover, the p -values of permutation tests reject the null hypothesis that these parameters are equal to zero.
In the case of Eq. Unlike the models of Eqs. Thus, all interpretations related to the behavior of the emissions obtained from the model Eq. The standard errors are calculated as in the previous case and the p -values of the permutation tests reject the null hypothesis that the model parameters are equal to zero Supplementary Fig.
In addition to the models of Eqs. This expression can also be related to the CES production by applying the Taylor series expansion to Eq. We have fitted Eq. In particular, Supplementary Fig. However, as shown in Supplementary Fig. The dataset used in this study were obtained from Gudipudi et al. All data supporting the findings of this study are available from the corresponding authors on reasonable request.
The code used for analyzing data is available from the corresponding authors on reasonable request. Schellnhuber, H. Avoiding Dangerous Climate Change. Cambridge University Press, New York, Google Scholar. Esch, T.
Breaking new ground in mapping human settlements from space—the global urban footprint. Remote Sens. Johansson, T. Cambridge University Press, Cambridge, Book Google Scholar. Seto, K et al. Climate Change Mitigation of Climate Change. Newman, P. Gasoline consumption and cities. Article Google Scholar. Lariviere, I. The area was confined not only because of lack of data in that areas, it is moreover because of equipments and financial constraints that faced the researcher during that period.
Because of the difficulty of making simultaneous measurements, a number of eighteen observers took measurements and readings. With the help of these observers, an intensive traverse surveys were carried out for measuring the air temperature, relative humidity and air velocity during one week period in December , starting in 20 th of the month and end by 26 th for one-hour duration per day from Local Malaysian Time LMT. The study area is divided into several sectors.
Each sector is assigned to one or two observers according to the area and complexity of the sector. The total number of sectors is Table 2. The results and analysis of the level of urbanization in terms of population density and land use, and the urban heat island are detailed below.
While by these population densities become for the city center and for the city of Kuala Lumpur. Furthermore, the expected population densities for are for KLMR, and for Kuala Lumpur city and the city centre of the city respectively. Thus, the highest population density is located in the city center of the city, then Kuala Lumpur city, while the less population density is in KLMR.
The population density of the city of KL has been increasing from in to in to in due to the increasing levels of urbanization of the city compare to its periphery. It rose because of the increasing number of migrants searching for better working opportunities, services, and facilities.
The residential and undeveloped land use of the whole city both decreased from By it occupied only 0. Conversely, the commercial, open space and recreational, and road and rail reserves land increased from 2. Almost there is no change in the industrial, institutional, cemetery, and educational land use of the whole city.
The industrial and institutional lands decreased from 2. While the cemetery, and educational lands increased from 3. The changes in the land use of the city center are almost following the same manner of the city of Kuala Lumpur. The commercial, road and rail reserves land increased from While the residential, industrial, and institutional land use reduced from In converse to the city increase in the open space and recreational land use, the city centre open space and recreational land use decreased from While the undeveloped land use of the city center increased from 0.
On the other hand, from previous studies, the intensity of the urban heat island of city of Kuala Lumpur in was 4. Comparing the previous values of the intensity of the UHI to this recent valued Table 3 below , the intensity increased from 4.
Thus, the increase is more than one degree Celsius, which is a recognized value whenever the human health and comfort are the issues.
Conversely the commercial, open space and recreational, road and rail reserves, cemetery, and educational lands increased. In addition to that, as the city centre get warmer and its temperature increased its commercial, undeveloped, road and rail reserves land increased, while its open space and recreational, residential, and institutional land decreased.
There is no contradiction between recent and previous findings of the first published similar work concerning UHI of the city that reported by Sham, a. The city centre still is the hottest area of the city of Kuala Lumpur. Such finding is due to continuous human activity and development within the city centre of KL. In the last two decades the city centre of KL experienced rapid changes in concentration of commercial activities and in the re-location of population.
The results of the study show that, the records of temperature for most of the stations located within the city center are recorded as the highest temperatures, while the records for the stations located within KL but outside the city centre are that of higher temperatures. On the other hand, the less heat and the high temperatures are register only for the stations located outside KL. Therefore, the higher the level of urbanization in terms of population density, the higher the temperature value recorded.
The City center has now been occupied by multi stories and tall buildings. These multi-storied buildings found in the city centers dominate the skyline, and have a dramatic effect on the microclimates of the city centre.
He has continually replaced vegetation and greenery with buildings. Furthermore, he has become a primary source of heat production from his transportation systems, industrial plants, and HVAC systems. Therefore, the city centre is still the hottest area of the city of Kuala Lumpur. On the other hand, the study shows that, all gardens and parks have relative low temperatures regardless of their locations, in or outside KL.
Furthermore, the lowest temperature is recorded for a station located within the city centre of the city, which is the Main Lake Garden station. That is because of the age and area of the garden compared to other gardens included in the study. The Main Lake garden is the largest lake park in the city Hamidah, This garden dates back to the s with an area of 73 hectares.
While Titiwangsa Lake garden is the second lake park in the city with an area of The garden is even different from other gardens in terms of its type and age of plants. Recent studies Elsayed, , show that, although the dependence of the intensity of the urban heat island of the city of KL on population density is significant, the population density at the city centre area is decreasing. It might be of interest to urban planners that, although the temperature is likely to rise with the increase of population density, the situation at the city centre is different.
This is due to the intensive human activity and development within the city centre of KL. That indicates that, the management of those lands is highly affecting the intensity of the urban heat island of such land.
The city centre experiences rapid changes in concentration of commercial activities and constructions. The city centre has been occupied by multi stories and very tall buildings e. Petronas Twin Towers. Man replaces vegetation and greenery by buildings and becomes a primary source of heat produce. Therefore, the city centre is still the hottest area of the city of Kuala Lumpur regardless of the reduction happened in its population density.
This fact should help in convincing urban planner and design makers in placing more emphasis on the strategies that relates the land management to the mitigation of urban heat island. The study shows that, the population density of the city is proportional to the records of temperature taken during the survey. A reliable water supply encourages a high population density as water can be used for drinking, washing, transportation and irrigation.
Locations with little or no infrastructure, including transport, energy, water and sanitation do not attract significant numbers of people. Poor transport infrastructure provides considerable challenges, especially with regards accessibility leading to a low population density.
Locations with an effective infrastructure, including transport, energy, water, and sanitation are usually densely populated. A good transport infrastructure attracts a high population density as people can travel and commute easily.
It also allows the free movement of goods which leads to the development of industry, providing jobs to people in the local area.
Civil war and persecution can lead to a low population density as people move to escape violence. A safe, reliable Government can encourage people to a country, leading to an increased population density. If you've found the resources on this page useful please consider making a secure donation via PayPal to support the development of the site. The site is self-funded and your support is really appreciated.
If you've found the resources on this site useful please consider making a secure donation via PayPal to support the development of the site. What factors affect population density and distribution? Human factors that affect population density include social, political and economic factors. Factors Sparsely populated Densely Populated Physical factors Mountainous areas make it difficult to construct buildings and roads.
They are often inaccessible and remote. Flood plains present the risk of flooding, so building on them is often prohibited. A lack of natural resources in an area presents significant challenges to economic development.
If land is infertile humans are unable to grow food leading to a low population density. Coastal environments and those with rivers provide good access and allow trading to occur, encouraging the growth of economic activities. Regions where the relief is flat are easier to build on and develop.
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