Socio-Environmental Agent-Based Simulation On The Livability Of Two Cities

Journal of Science, Engineering and Technology 4:42-48 (2016)Southern Leyte State University, Sogod, Southern Leyte, PhilippinesSocio-Environmental Agent-Based Simulation on theLivability of Two CitiesJuvyneil E. Cartel* & Wilfren A. ClutarioOffice of Research, Development, and Extension Services (ORDEx)Eastern Visayas State University, Tacloban City, PhilippinesAbstractThe need for methodological advances in research as an important tool for instantaneousand better understanding on the dynamic and heterogeneous behavior in socio-environmentalsystems is increasing. In this paper, the greenhouse gas (GHG) emissions and mitigationrates, and their effect to human inhabitants of two mega cities namely, Metro Manila,Philippines and New York City, USA were studied. A simple framework to develop agent-basedsimulations using NETLOGO version 5.2.1 systematically was conducted. Factual data wasintroduced and then considered for the application for a dynamic model of GHG emissions andits effect to human population considering the mitigation programs implemented. The modelcame out to be reliable in projecting the livability of the two mega cities with a margin of errorof approximately 7%. Results showed that Metro Manila has higher livability compared toNew York City by 6%. The results are quite alarming which suggest more involvement fromthe human inhabitants to GHG mitigation programs considering that the livability of the citiesare mainly dictated by the population growth as observed. However, it has to be noted thatthe chance of involvement of the population should not go beyond the carrying capacity of anecosystem.Keywords: Greenhouse gases (GHG); Livability; Metro Manila; New York CityIntroductionhas been identified to come from differentemission sources.They are categorizedeither as mobile, area, or stationary sources(EMB-NCR, 2011).Mobile sources mayinclude running emissions, cold start, hotstart, exhaust, and evaporative emissions(EMB-NCR, 2011). Area source emissionsmay come from structural and automobile fireswhile stationary sources come mainly fromthe combustion of residual oils (EMB-NCR,2011). Major global problem dealing withair pollution is less recognized neglecting itspossible effect to the livability of an area (Giapand Yew, 2014).Air pollution has long been recognizedas a potentially lethal form of pollution.Greenhouse gases (GHG) emissions maycompose carbon monoxide (CO), carbondioxide (CO2), NOx, SOx, and particulatematters (PM) (Ledley et al., 1999). GHG areprimary contributors of air pollution warms thesurface and the atmosphere with significantimplications for rainfall, retreat of glaciersand sea ice, sea level, among other factors(Ramanathan and Feng, 2009). About 30years ago, it was recognized that the increasein tropospheric ozone from air pollution (NOx,The need for methodological advancesCO and others) is an important greenhouse in research as an important tool forforcing term (Ramanathan and Feng, 2009). instantaneous and better understandingGreenhouse gases (GHG) in urban areas on the dynamic and heterogeneous behavior*Correspondence: juvyneilcartel@gmail.comISSN 1908-6512

Cartel & ClutarioJSET Vol. 4, 2016in socio-environmental systems is increasing.An approach to use agent-based simulationon a computer system is considered tobe an effective method.Simulation withdynamicallyinteractingheterogeneousagents is expected to re-produce complexphenomena in air pollution and mitigation.This also helps to test various controllingmethods, evaluate systematic designs, andobtain fundamental elements which produceinteresting phenomena for future analyticalwork (Mizuta and Yamagata, 2001).Related literatures and current livabilityprograms shows that livability is generallythought of as having multiple dimensions.Livability is derived from the word “livable”which means broadly as “suitability for humanliving” (Merriam-Webster, 2017). For instance,a definition provided by the Victoria TransportPolicy Institute (VTPI) claims that livabilityis affected by a community’s public safety,environmental quality, community cohesion,friendliness, aesthetics, accessibility, pride,and opportunity” (VTPI, 2010). In addition,types of livability objectives may include(1) environmental goals (such as airquality, open space, and greenhouse gasemissions); (2) economic goals (such aseconomic revitalization and development),land use goals (such as compact, mixed usedevelopment); (3) transportation goals (suchas walkability, accessibility, and transportationchoices); (4) equity goals (such as affordablehousing and mixed-income communities);and (5) community development goals (suchas sense of place, safety, and public health)(Fabish and Haas, 2010).In this paper, the greenhouse gas emissionsand mitigation rates, and their effect tohuman inhabitants of two mega cities namely,Metro Manila, Philippines and New York City,United States of America were studied. Asimple framework to develop agent-basedsimulations using the open source, NETLOGOversion 5.2.1 systematically based on factualdata gathered was introduced and thenconsidered for the application for a dynamicmodel of greenhouse gases (GHG) emissionsand its effect to human population consideringthe mitigation programs implemented. Thisstudy aims for early prediction of livability oftwo mega cities and eventually do somethingout of it (e.g. increase mitigation measures)at an earliest possible time considering thepopulation growth rate, mitigation programs ofthe government, and GHG emission rates.MethodologyParameters. Parameters are classified asset values and process values. Set valuesinitially provided on the NETLOGO version5.2.1 came from factual data available onreputable sources as of year 2010. Theseinclude initial population, average populationgrowth rate per annum (in percent), numberof greenhouse gases (GHG) sources, GHGemission rates (in metric tons per annum),and mitigation measures or programs (fromthe budgetary allotment of an implementingagency of a government, in percent). On theother hand, process values include count ofpeople and the number of years (correspondto the projected livability of a city).How it Works. This model has its rootsfrom the predator-prey equilibrium conditionin an ecosystem wherein agents representingpeople, GHG pollutants and mitigationelements compete for resources. Dynamicinteractions of these elements throughtime can be explored through this model.Behaviors of multiple generations of agentscan be analyzed:irregular reproductivesuccess of the population of people whichgenerates regular oscillations in populationsize might lead to possible extinction; pollutioninhibit the population density while the numberof emitters and rate of emission stimulate theincrease in pollution.In the work of Felsen and Wilensky (2007),‘Power Plants’ indicate pollution sources,which releases pollution into the environment.In this work, ‘Emission Sources’ also createpollution but is now a general term whichinclude stationary sources, mobile sourcesand area sources.The reproduction of43

Cartel & ClutarioJSET Vol. 4, 2016Figure 1. Logical flowchart of agent-based model simulationthe population is adversely affected by thispollution and those who create children isgoverned by the ‘Growth-rate’. This factor alsogives an idea on the cloning rate for naturaldeaths.The population can think of ways to alleviatepollution generation and was representedin the work of Felsen and Wilensky by‘Planting-rate’. In this work, this mitigationactivity is now represented by ‘Mitigation-rate’and signifies the budget allocation in percentfor the implementation of pollution mitigationactivities or researches.Stability of theecosystem is achieved if neither the peoplepopulation nor the pollutants overrun theenvironment. The number of ticks representthe number of years that a certain can belivable. Livability is defined as the period oftime that a certain area (e.g. city) could sustaingood quality of air to human inhabitants. Thefollowing rules were adopted from Felsen andWilensky (2007):441. GHG emission sources are grid cellswith a very high fixed pollution value(determined by the GHG emission rate).2. All grid cells have some GHG emissionwhich is a major indicator value forpollution, although it may be 0. Pollutiondiffuses throughout the grid, so each gridshares part of its pollution value withits neighboring cells. Since the GHGemission is fixed at a high amount atGHG emission sources, this has the effectthat pollution emanates out from the GHGemission sources.3. Mitigation programs by the government(determined by mitigation rate), however,clean up pollution in the cell they areplaced, and the neighboring cells. Thus,they block the spread of pollution,by emanating low-pollution values.Mitigation programs are implemented ina set period of time.

Cartel & ClutarioJSET Vol. 4, 2016Table 1. Preliminary data for agent-based pollution model simulation as of year 2010.ParameterAreaInitialPopulationMetro ManilaNew York City11, 855,975 (a)8,175,133(d)Ave.PopulationGrowthRate (in%)1.78 (a)1.30(d)AnnualMitigationRate(in%)Annual GHGemissionsourceAnnual GHGemissionsource9.50(b)6.006,641,181 c1,972,127(f)14,022,070 c54,300,00(g)Notes:(a) – (NSO-NCR, 2012)(b) – (DENR-NCR, 2016); estimated(c) – (EMB, 2012)(d) – (NYC, Planning 2014)(e) – (Page, 2015); estimated(f) – (Charles-Guzman, 2012)(g) – (Dickinson and Tenorio, 20114. Each time step (tick) of the model, people have improved the chance of involvement ofhuman factor in the area in mitigating airagentspollution which could increase the livability ofa) move randomly to an adjacent cella city.b) with some probability, they may plantThe snapshot of the process output showna landscape elementby NETLOGO version 5.2.1 is shown onc) if they are healthy enough, with some Figures 2 and 3 for Metro Manila and New Yorkprobability, they may reproduce City, respectively.Sensitivity Analysis.To enable to(clone)determine which factor plays an importantd) if their health has dropped to 0, theyrole on the livability expectancy of the twodie.mega cities, sensitivity analysis for each wasThe logical process on how the agent-based conducted by increasing a single criteria by 60model simulation works is shown on Figure 1. percent for each run. Results were consistentInput. Preliminary data were gathered to for both mega cities that the variance underprovide input on the pollution model, shown in Population Growth Criteria has the highestTable 1. These are the data sets that will be variance among others indicating that thiscriteria is the most sensitive among others.used to run the model.It must also be noted that the simulationprocess assumes that all human inhabitantsResults and Discussionthat comprise the population growth areProcess Output. After running the model all participating in the GHG interventionfor 500 runs, starting from year 2010, and programs.It can be implied that the population growthcalculating the average livability in years foreach mega city, it was found out that Metro of a city plays a vital role in increasing theManila has higher livability compared to New livability of a city considering the chance ofYork City by 6.11% with an average livability their involvement in mitigating air pollution.of 57 and 54 years, respectively. This means Provided however, that that chance ofthat Metro Manila and New York City can be involvement of the population will not golivable until year 2067, and 2064, respectively. beyond the carrying capacity of a certainThis may be due to higher initial population, ecosystemValidation. The result of the simulationgrowth rate, and GHG mitigation measuresobserved in Metro Manila than in New York for each city was validated to determine theCity. Furthermore, higher growth rate may degree of reliability of the simulation results45