Carbon Reduction Potential of Resource-Dependent Regions Based on Simulated Annealing ...

17 Pages·2017·1.86 MB·English

sustainability
Article
Carbon Reduction Potential of Resource-Dependent
Regions Based on Simulated Annealing
Programming Algorithm
WeiLi*,GuominLi,RongxiaZhang,WenSun,WenWu,BaihuiJinandPengfeiCui
SchoolofEconomicsandManagement,TaiyuanUniversityofTechnology,Taiyuan030024,China;
[email protected](G.L.);[email protected](R.Z.);[email protected](W.S.);
[email protected](W.W.);[email protected](B.J.);
[email protected](P.C.)
* Correspondence:[email protected];Tel.:[phone]
Received:18May2017;Accepted:30June2017;Published:3July2017
Abstract: Inrecentyears,developingcountries,especiallyresource-dependentregions,havebeen
facingtheparadoxofensuringbothemissionsreductionandeconomicdevelopment. Thus,thereisa
strongpoliticaldesiretoforecastcarbonemissionsreductionpotentialandthebestwaytoachieveit.
Thisstudyconstructsamethodologytoassesscarbonreductionpotentialinaresource-dependent
region.TheSimulatedAnnealingProgrammingalgorithmandtheGeneticalgorithmwereintroduced
tocreateapredictionmodelandanoptimizedregionalcarbonintensitymodel,respectively. Shanxi
ProvinceinChina,atypicalresource-dependentarea,isselectedfortheempiricalstudy. Regional
statisticaldataarecollectedfrom1990to2015. TheresultsshowthatthecarbonintensityofShanxi
Province could drop 18.78% by 2020. This potential exceeds the 18% expectation of the Chinese
Governmentinits‘13thFive-YearWorkPlan’forControllingGreenhouseGasEmissions. Moreover,
thecarbonintensityoftheprovincecouldbefurtherreducedby0.97tper10,000yuanGDP.The
study suggests that the carbon emissions of a resource-dependent region can be reduced in the
followingways;promotingeconomicrestructuring,upgradingcoalsupply-sidereform,perfecting
the self-regulation of coal prices, accelerating the technical innovation of the coal industry, and
establishingaflexiblemechanismforreducingemissions.
Keywords: resource-dependent regions; carbon reduction potential; carbon intensity; Simulated
AnnealingProgramming;ShanxiProvince
1. Introduction
The growing concentrations of greenhouse gases (GHGs), especially in resource-dependent
regions,hasbeenoneofthemajorglobalchallengesasitbringsaboutenvironmentaldegradation
and natural disasters threatening human safety and health [1]. To reduce its influence and avoid
more dangerous long-term effects, the Intergovernmental Panel on Climate Change (IPCC) called
forlimitingtheincreaseinaverageglobaltemperaturetonomorethan2 ◦C.Tothisend, wemay
need to see a reduction in carbon dioxide (CO ) by at least 50% until 2050, which means that any
2
futureemissionsleewaycouldbecomeextremelyconstrainedaccordingtoPanetal.[2]. Consequently,
thereisastrongpoliticaldesiretoforecastcarbonemissionsreductionpotentialandthebestmeansof
achievingit[3]. Accordingly,weneedtoidentifythefactorsinfluencingcarbonreduction,understand
in depth the mechanism of their effects, create a series of predictive models, and optimize carbon
reductionpotential.
Theexistingresearchoncarbonreductionpotentialmainlycoversthefollowing;carbonemissions
measurements[4–7],carbonemissionsimpactfactorsandmechanismanalysis[8–14],scenarioanalysis,
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carbonemissionspredictions[15–19],andpolicysimulations[20–22]. Thekeytoestablishingcarbon
emissionsreductionpoliciesandrealizingthesituationliesintheidentificationandanalysisofcarbon
impactfactorsandtheirunderlyingdrivingmechanisms. Existingresearchshowsthatmanyfactors
affectcarbonemissions,ofwhichthemostimportantonesincludeeconomicdevelopment,industrial
structure,technologicaladvance,energyprices,andsocialinvestment.Almulalietal.[23]investigate30
SubSaharanAfricancountriesusingpaneldata. Theirresultsshowthateconomicdevelopmentisthe
centraldrivingfactorbehindincreasingcarbonemissionsintheeconomiesinvestigated. Almulali[24]
investigatesthemajorfactorsthatinfluencedcarbonemissionsin12MiddleEasterncountriesand
showedthatsocialinvestmentandGDPwerethemostimportantfactorsincreasingcarbonemissions
inthesecountries. Tuncetal.[9]identifiedthefactorsthatcontributetochangesinCO emissionsin
2
theTurkisheconomybyutilizingtheLogMeanDivisiaIndex(LMDI)method. Theresultsshowed
thatthemaincomponentinTurkeythatdetermineschangesinCO emissionsiseconomicactivity.
2
De Freitas et al. [10] examines a decoupling of the growth rates of economic activity and CO
2
emissions and energy consumption in Brazil from 2004 to 2009. Their results indicate that carbon
intensityandenergymixwerethemaindeterminantsofemissionsreductioninBrazil. Lietal.[25]
discussthedrivingforcesinfluencingChina’sCO emissionsbasedonthePath–STIRPATmodel,a
2
methodcombiningpathanalysiswithstochasticimpactsbyregressionsonpopulation,affluence,and
technology (STIRPAT). The analysis showed that GDP per capita was the main factor influencing
China’s carbon emissions. Yao et al.’s [26] studied on the factors affecting energy consumption in
Chinaandmajorindustriesrecognizesthatenergypriceswerethemostimportantfactorinfluencing
abatementcosts. Wuetal.[27]usedseveralenvironmentaldataenvelopmentanalysis(DEA)models
withcarbonemissions,showingthattheenergyefficiencyimprovementinChina’sindustrialsector
is mainly driven by technological improvement. Li et al. [28] discuss the regional differences in
impactfactorsoncarbonemissionsusingtheSTIRPATmodel. Theirresultsindicatethatimproving
technologylevelsproducesasmallreductionincarbonemissionsinmostemissionsregions. Overall,
the factors that affect carbon emissions can be summarized as economic development, industrial
structure,technologicaladvance,energyprices,andsocialinvestment.
Amongtheexistingstudies,researchoncarbonemissionsreductionpotentialhasmainlyfocused
onaglobal[29–31],regional[32],ornational[33–35]macroscale,whilestudiesonasmallormedium
scale (such as provincial or urban) are still relatively rare. Domestic research in China on factors
affectingcarbonemissionsattheprovincialandmunicipallevelshasonlyrecentlyappeared. From
the perspective of geography, there are significant differences in population growth, household
consumption,economicdevelopmentlevel,energyresourceadvantagesandtechnicallevelsamong
provinces and among the eastern, central, and western regions of China. Thus, it is important to
reveal the multi-factor interaction mechanism of carbon emissions that may be masked by such
regionaldifferences.
Intermsofresearchmethods,aneconomicmodelisthemostfrequentlyusedmeansofanalyzing
eachdrivingfactorthathasanimpactoncarbonemissionsorthepotentialtoreducecarbonemissions
(specificmodelsfocusonstructureorindexdecomposition).O’MahonyusesanextendedKayaidentity
as the scheme and applies the LMDI as the decomposition technique to decompose change in the
carbonemissionsofIrelandfrom1990to2010[12]. InapplyingtheSTRIPATmethod,Liddleetal.[36]
comparethecarbonelasticityofincomeandpopulationofOrganizationforEconomicCo-operation
and Development (OECD) countries with that of non-OECD countries. In another recent study,
Wangetal.[37]analyzetheinfluencingfactorsofcarbonemissionsinenergyconsumptioninSuzhou
Citybyusinganindexdecompositionmodel. LiangandZhang[38]analyzetheimpactfactorsof
carbon emissions in a manufacturing industry based in the eastern coast of Jiangsu Province and
showthatreductioninenergyintensityandimprovementinenergyconsumptionstructurearekeyto
achievingthegoaloflow-carbonemissions.
In these studies, a potential assumption in either structural decomposition or exponential
decomposition models is that the effects of the driving factors on carbon emissions are isolated
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andunrelated. However,thisassumptionisimpractical. WangandYang[39]andZhangetal.[40]
studythefactorsaffectingcarbonemissionsindifferentlevelsinChina. Theresultsdemonstratethat
thedrivingfactorsofcarbonemissionsaffectedoneanother. Toseparatetheinteractions,apartialleast
squaresregressionandaprincipalcomponentanalysisareusedinsomestudies[41,42]. However,the
clusteringprocessbringsnewproblems. Thelossoftheoriginalinformationcanbeinconvenientto
subsequentpolicydevelopment. Therefore,acertainmethodologysystemneedstobeconstructed
thatnotonlytakesintoaccounttheinteractionsamongfactorsbutalsoretainstheinformationofthe
originalindicators.
This study contributes to the literature in the following ways: (1) The Simulated Annealing
Programming(SAP)algorithmisintroducedintocarbonreductionresearchtoassesscarbonreduction
potential. Asaresult,anewmethodologicalsystemisconstructedthatcoversgapsinexistingstudies.
(2)InthecaseexampleofShanxiprovinceinChina,thisstudyanalyzesthecarbonreductionpotential
of a resource-dependent region. The results provide evidence for policy makers in such regions.
(3)ThisstudypredictsthefeasibilityofthecarbonreductiongoalsintheChinese‘13thFive-YearWork
Plan’and,further,quantitativelycalculatesthepotentialofcarbonreduction.
Theremainderofthispaperisorganizedasfollows. Section2introducesthepredictionmethod
forcarbonintensitybasedontheSAP.Section3presentstheempiricalanalysisandresults,andfinally
Section4discussestargetedsuggestionsforthepolicy-makingprocessasrelevanttocarbonreduction.
2. MaterialsandMethods
As the carbon emissions system is a typical composite system composed of a natural and
an artificial system, it has the characteristics of openness, being away from an equilibrium state,
non-linearity, and the existence of random fluctuation. In order to study the mutual influence of
each impact factor in the composite system, to adapt to environmental changes due to economic
developmentandchanges,and,ultimately,tobeareasonablestate,thisstudyusescomplexsystem
theoryandmethods,includingselectingcarbonintensityasthesystemdependentvariableandthe
influencingfactorsastheindependentvariables.Tosolveregionalcarbonemissionsreductionpotential,
weconstructamodeltocapturethedynamicquantitativerelationshipbetweencarbonintensityand
thesystemelementsandwethenexaminethepredictionandoptimizationofcarbonintensity.
2.1. PredictionMethodofCarbonIntensityBasedontheSimulatedAnnealingProgramming
Duetothecomplexityofcarbonemissionsreduction,itisverydifficulttoestablishadynamic
model that describes the evolution of the emissions reduction system. The traditional method is
limitedbytherationalityofthesystemstructure,thecomplexityoftheparametercalculations,andthe
accuracyofthefinalconclusion. TheSAPisastochasticsearchalgorithmthatissuitableforsolving
large-scalecombinationoptimizationproblems[43].Itcanovercomenonlinear,multi-dimensional,and
complexsystemmodelsandalsodeterminethedifficultstructureoftheequation[44]. Thesimulated
annealingprogramminghasbeenappliedinthefieldsofproductionscheduling,controlengineering,
machinelearning,neuralnetworks,andsignalprocessing,amongothers. Thisstudyintroducesitinto
thefieldofcarbonemissionsprediction.
2.1.1. SimulatedAnnealingProgrammingAlgorithm
The algorithm is derived from the enlightenment of ‘annealing’ physical phenomena in
thermodynamics. The lower the temperature is, the lower the energy of the object is; when the
temperatureislowenough,theliquidbeginstocondenseandcrystallize. Inthecrystallinestate,the
system’senergystatelowerstoaminimum[45].
TheSAPalgorithmisbasedonthesimulationofthesolidannealingprocess.UsingtheMetropolis
acceptance criterion, a set of parameter control algorithm processes, namely the cooling schedule,
isestablishedtogivethealgorithmanapproximateoptimalsolutioninpolynomialtime[46]. The
physicalimageandstatisticalpropertiesofthesolidannealingprocessarethephysicalbackgroundof
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theSAP.TheMetropolisacceptancecriterionguaranteesthealgorithmwillescapethelocaloptimal
background of the SAP. The Metropolis acceptance criterion guarantees the algorithm will escape the
‘trap’,andthereasonablechoiceofthecoolingscheduleistheprerequisitefortheapplicationofthe
local optimal ‘trap’, and the reasonable choice of the cooling schedule is the prerequisite for the
algorithm [47]. The flow chart of the Simulated Annealing Programming Algorithm is shown in
application of the algorithm [47]. The flow chart of the Simulated Annealing Programming Algorithm
Figuirse s1h.own in Figure 1.

Figure 1. The flow chart of the Simulated Annealing Programming Algorithm.
Figure1.TheflowchartoftheSimulatedAnnealingProgrammingAlgorithm.
It is calculated by the initial solution and the initial value of the temperature control parameters;
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controlled by the cooling schedule parameters, including the initial value of the control parameter
solutionattheendofthealgorithmistheapproximateoptimalsolution. Theannealingprocessis
and its attenuation factor, the number of iterations per value (called the length of a Markov chain),
controlledbythecoolingscheduleparameters,includingtheinitialvalueofthecontrolparameterand
and the stop condition.
itsattenuationfactor,thenumberofiterationspervalue(calledthelengthofaMarkovchain),andthe
The SAP has the following characteristics [48]: (1) to avoid the loss of the local optimal solution,
stopcondition.
we accept the worse solution with a certain probability. (2) To improve the reliability of the optimal
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solution,theSAPalgorithmslowlyreducesthecontrolparameters,improvingtheacceptancecriterion
untilthecontrolparameterstendtozero. (3)Theobjectivefunctionrequirementsarefewerandare
notsubjecttocontinuousmicro-constraints,underwhichthedefinitiondomaincanbearbitrarilyset.
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(4)Toimprovethequalityandoperationspeedofthesolution,theintroductionofimplicitparallelism
isemployedtosolvethenonlinearproblem.
2.1.2. TheoreticalModelandVariableSelection
Thetheoreticalmodeltosolvethenonlinearmodelofregionalcarbonintensityandthekeyfactors
isasfollows:
CI = f(X ,X ...X ...X ) (1)
1t 2t it nt
whereCIisthevalueofregionalcarbonintensity,X isthet-thyearofthei-thkeyfactor,andf(x)isa
it
non-linearfunction. ThekeyinfluencingfactorsareenteredintotheSAPalgorithminordertofindthe
quantitativerelationshipbetweenregionalcarbonintensityandeachinfluencingfactor.
Fiveinfluencingfactorsofcarbonintensity,includingeconomicdevelopment,industrialstructure,
technologicaladvances,energyprice,andsocialinvestment,areselectedasindependentvariablesfor
theirsignificance. Themechanismofactionisdescribedasfollows.
(1) EconomicDevelopment. AccordingtoNagandParikh[49]andAng[50],thereisaninverted
U-shaped relationship between economic development and carbon intensity. That is, carbon
intensityincreaseswithgrowingGDPuptoathresholdlevelbeyondwhichcarbonintensity
dropswithhigherGDP.Thedevelopmentoftheeconomynotonlyincreasesthetotalamount
ofcarbonemissions, butalsoimprovesenergyefficiencyandreducescarbonintensitylevels.
Thus,theeconomydevelopmentprovidesthebasicsupportforthedevelopmentoftechnology,
layingastrongfoundationforthereductionofcarbonemission. ThisstudyusespercapitaGDP
indicatorstocharacterizethelevelofeconomicdevelopment.
(2) IndustrialStructure. YangandLiu[51]believethattheindustrialstructureisanimportantfactor
indifferencesincarbonemissionslevels;Gao[52]believesthattheindustrialstructure,thelevel
ofindustrialization,anditsopennesstothepublichaveasignificantimpactoncarbonintensity.
ThisstudyusestheproportionoftertiaryindustrytoGDPtocharacterizetheimpactofindustrial
structureoncarbonintensity.
(3) TechnologicalAdvances. Itwasgenerallyconsideredthattechnologicaladvanceshaveapositive
effectonCO emissionsreduction[53–55]. Theeffectismainlymanifestedintwoways. First,
2
technologicaladvancescanimprovetheefficiencyofmechanicalequipment,increasetheuse
ofartificialproficiency,andincreaseoutputinsimpleways,directlyreducingcarbonintensity.
Secondly,technologicaladvancescanimproveunitenergyoutput,increasetheefficiencyofthe
use of social resources, and thereby reduce the social needs of products, product prices, and
overallinvestment,forcingtheeliminationofexcesscapacityandtheadjustmentoftheindustrial
structure,evenlyreducingcarbonintensity. Inthisstudy,totalproductivityindicatorsareusedto
characterizetheimpactofenergytechnology.
(4) EnergyPrice. Greeningetal.[56]believedthatenergypriceshadasignificantimpactoncarbon
intensityindirectorindirectways. ChenandTong[55]arguedthatthedirectimpactofenergy
priceswaslow. Asanindirectimpact,energypriceshaveaneffectmainlybyadjustingtheenergy
consumptionstructure,therebyindirectlyreducingcarbonintensity. Asadirectimpact,arise
inpricescanencouragecompaniestoreducecarbonintensity,reducetheirquantityofuse,and
utilizecleanalternativeenergysourcestoimproveefficiency. Thisstudyusesrawmaterials,fuel,
andthepowerpurchasepriceindexasindicatorstocharacterizeenergyprices.
(5) SocialInvestment. Researchshowsthattheexpansionofinvestmentlevelsoncarbonemissions
hasbidirectionaleffectsoncarbonintensity[57,58].Ontheonehand,theexpansionofinvestment
can increase total production scale, thus increasing carbon intensity; on the other hand, the
investmentincreasecandriveeconomicdevelopment,whichplaysapositiveroleinpromoting
technologicalinnovation. Formerstudiesshowthatamodestincreaseininvestmentcanpromote
anincreaseincarbonintensity,butexcessivegrowthiscounterproductive. Thisstudyusestotal
societalfixedassetinvestmentastheindicatortocharacterizethelevelofsocialinvestment.
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(i) Coding. The problem is described as a string through the fitness calculation, wherein each
(i) Coding. The problem is described as a string through the fitness calculation, wherein each
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1t 2t nt
(ii) Forming the initial group. The initial population is generated by random method, indicating
(ii) Formingtheinitialgroup. Theinitialpopulationisgeneratedbyrandommethod,indicating
some things such as a random chromosome, the fixed number of groups, and the project is set
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to 100.
to100.
(iii) Calculating the fitness. The fitness is an indicator of measuring the solution (chromosome) as
(iii) Calculatingthefitness. Thefitnessisanindicatorofmeasuringthesolution(chromosome)as
good or bad and relates to the objective function of the algorithm model. The project fitness
good or bad and relates to the objective function of the algorithm model. The project fitness
calculation function is fitness = f(X1t, X2t, …, Xnt); the corresponding objective function is CIoptimization
calculation function is fitness = f(X , X , ... , X ); the corresponding objective function is
1t 2t nt
= Min(fitness).
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optimization
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Sustainability 2017, 9, 1161 7 of 17
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rreedduuccttiioonn ppootteennttiiaall ooff rreessoouurrccee--ddeeppeennddeenntt rreeggiioonnss iinn aa ttyyppiiccaall rreeggiioonn wwiitthh aa rreessoouurrccee--bbaasseedd eeccoonnoommyy..
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mSAoPdeml. odel.
3.1. StudyArea
3.1. Study Area
ShanxiProvinceislocatedinthemiddleoftheYellowRiverBasin,whichhasapopulationof
Shanxi Province is located in the middle of the Yellow River Basin, which has a population of
36.64millionandcoversanareaof156,700squarekilometers. Shanxiisthelargestcoalproduction
36.64 million and covers an area of 156,700 square kilometers. Shanxi is the largest coal production
andtransportationprovinceaswellasanenergyandheavychemicalindustrybaseinChina. Thetotal
and transportation province as well as an energy and heavy chemical industry base in China. The
areaofcoal,whichaccountsforabout40%oftheprovince,is64,800squarekilometers. Relyingon
total area of coal, which accounts for about 40% of the province, is 64,800 square kilometers. Relying
coalresources,theShanxieconomyhasachievedrapiddevelopment,but,atthesametime,ithasalso
on coal resources, the Shanxi economy has achieved rapid development, but, at the same time, it has
createdthephenomenonofa‘coaleconomy’. Thevolatilityofcoalpriceshasledtoswingsinregional
also created the phenomenon of a ‘coal economy’. The volatility of coal prices has led to swings in
economies(asshowninFigure3).
regional economies (as shown in Figure 3).

Figure 3. An overview of economic development in Shanxi Province in China. Note: the data for GDP
Figure3.AnoverviewofeconomicdevelopmentinShanxiProvinceinChina.Note:thedataforGDP
and industrial value-added are obtained from the Shanxi Statistical Yearbook (1979–2015).
andindustrialvalue-addedareobtainedfromtheShanxiStatisticalYearbook(1979–2015).
At the same time, even more unsettling is that coal resources have brought about environmental
pollution and carbon emissions, which has led to Shanxi Province becoming one of the most carbon-
Sustainability2017,9,1161 8of17
Atthesametime,evenmoreunsettlingisthatcoalresourceshavebroughtaboutenvironmental
pollution and carbon emissions, which has led to Shanxi Province becoming one of the most
carbon-dense regions in China. A development model characterized as long-term and highly
dependentoncoalresourceshasraisedaseriesofproblemsandcontradictionsbycreatingregional
resourcewaste,capitaloutflows,theweakeningofinstitutions,andadecreaseininnovationcapacity.
Allofthisseriouslyrestrictsthedevelopmentofthelocalsocietyandeconomics. Thus,thestudyof
carbonemissionsreductionpotentialinShanxiProvinceisagreatreferencepointforthetheoryand
practiceofcarbonreductioninresource-dependentareas.
3.2. PredictionModelofCarbonIntensityforShanxiProvince
Inthissection,thepredictionmodelofcarbonintensityforShanxiProvincewasdeducedthrough
theSAPalgorithm. ThevaluablevaluesoftheSAPalgorithmweredeterminedandthedatarequired
werecollected. Moreimportantly,afittestwasconductedtoverifythevalidityofthepredictionmodel.
3.2.1. VariableSettingandDataResources
ThecarbonintensityinShanxiProvincewasselectedasthedependentvariable,expressedasCI;
theinfluencefactorsoncarbonintensitywereselectedastheindependentvariables. X ,thepercapita
0
GDP,isanindicatorofeconomicdevelopment;X ,theproportionofthetertiaryindustryinGDP,isan
1
indicatorofindustrialstructure;X ,thetotalfactorproductivity,isanindicatorofenergytechnology;
2
X ,therawmaterial,fuel,andpowerpurchasepriceindex,isanindicatorofenergyprices;andX ,
3 4
thetotalsocietalinvestmentinfixedassets,isanindicatorofsocialinvestment. ThevaluesofCIand
X areshownasTable1.
i
Table1.TheCarbonIntensityanditsinfluencefactorsinShanxiProvincefrom1990to2015.
Years (tCarbon/C10I,000Yuan) (YuanX/P0erson) X1 X2 X3 (10,00X04Yuan)
1990 11.62 1480.78 32.24 0.202 100.00 123.41
1991 10.83 1520.40 34.31 0.200 108.01 133.04
1992 9.40 1689.20 34.80 0.188 120.86 155.39
1993 9.51 1888.87 34.92 0.171 164.25 193.93
1994 9.39 2060.50 34.89 0.176 189.06 210.02
1995 13.35 2284.24 35.04 0.188 213.82 224.31
1996 12.32 2526.75 35.09 0.199 224.08 235.30
1997 10.68 2784.72 35.76 0.221 228.57 238.83
1998 10.03 3030.79 35.85 0.241 222.40 235.96
1999 8.61 3218.80 36.63 0.264 215.72 235.25
2000 8.23 3472.89 36.83 0.283 219.82 239.49
2001 7.94 3796.03 37.65 0.308 223.78 243.56
2002 8.44 4256.35 37.09 0.353 229.60 244.78
2003 8.36 4858.86 37.30 0.400 247.51 251.87
2004 7.98 5564.06 37.50 0.448 283.39 264.97
2005 8.06 6277.36 37.43 0.509 306.63 272.92
2006 7.98 7040.28 36.50 0.582 314.61 277.01
2007 7.37 8116.32 36.63 0.661 331.28 288.37
2008 6.56 8759.57 38.01 0.665 391.90 326.73
2009 6.12 9194.59 40.11 0.637 378.58 320.52
2010 5.84 10,047.33 38.61 0.753 412.65 332.38
2011 5.78 11,280.38 37.29 0.871 446.08 350.66
2012 5.46 12,361.70 37.32 0.924 437.60 354.87
2013 5.21 13,391.54 37.02 0.981 417.91 356.64
2014 5.07 13,977.79 37.80 0.996 419.33 355.21
2015 4.87 14,278.4 38.78 1.000 439.47 348.82
Sustainability2017,9,1161 9of17
Consideringtheavailabilityofdataandthedifferenceinthestatisticalcaliberofeachyear,this
study chooses time-series data from 1990 to 2015. The original data of variables in Table 1 were
collectedfromtheShanxiStatisticalYearbook(1991–2016)andtheChinaEnergyStatisticalYearbook
(1991–2016)andwerestandardizedtoeliminatetheeffectofdimensions. ThedetailedCIcalculation
andthecarbonemissionscoefficientsoffossilfuelsareprovidedinAppendixA.Thedetailedtotal
factorproductivitycalculationsandrawdataareprovidedinAppendixB.
3.2.2. PredictionModelofCarbonIntensity
ThedatainTable1areinputintotheSAPmodelgiveninSection2.1. Theparametersaredefined
as: initialtemperatureT =200,terminationtemperatureT =0,andtemperaturedropcoefficient
0 min
α=0.95. TheresultingfitcoefficientR2is0.91,andthefittedequationisgivenasfollows:
x4+99.96 −0.014x +x +395.67
CˆI = x3−95.78 0 2 (2)
2x −27.676− x3−99.81
1 57.04x2
3.2.3. ModelFitTest
Inthissection,goodnessoffittestsareperformedtoverifythesuperiorityoftheSAP,andthe
resultsareshowninTable2.
Table2.GoodnessofFittestsfortheSimulatedAnnealingProgrammingmodel.
SimulatedAnnealing SimulatedAnnealing
Years ProgrammingFitResults Years ProgrammingFitResults
FitValueCˆI Residualεˆ FitValueCˆI Residualεˆ
1990 11.637 0.021 2003 8.169 −0.196
1991 9.785 −1.044 2004 7.978 0.002
1992 9.570 0.171 2005 7.740 −0.319
1993 10.508 0.993 2006 7.706 −0.273
1994 11.148 1.758 2007 7.210 −0.158
1995 11.538 −1.809 2008 6.769 0.207
1996 11.509 −0.808 2009 5.997 −0.119
1997 10.689 0.007 2010 6.083 0.241
1998 10.147 0.114 2011 6.007 0.230
1999 9.333 0.722 2012 5.545 0.082
2000 9.081 0.853 2013 5.177 −0.035
2001 8.517 0.576 2014 4.785 −0.281
2002 8.462 0.017 2015 4.509 −0.361
AswecanseeinTable2andEquation(2),themaximumabsoluteresidualoftheSAPmodelis
1.809in1995,theminimumabsoluteresidualis0.002in2004,andthefittingdegreeis0.91,which
is greater than 0.9. Thus, the advantages and feasibility of using the SAP model to construct the
predictionmodelofcarbonintensitycanbeverified. Thefittingchartofthecarbonintensityprediction
modelisshownasFigure4. Obviously,thetrendlineofthepredictionvalueissimilartothatofactual
valueinFigure4.
Sustainability2017,9,1161 10of17
Sustainability 2017, 9, 1161 10 of 17

Figure 4. Fitting chart of the carbon intensity prediction model.
Figure4.Fittingchartofthecarbonintensitypredictionmodel.
3.3. Analysis on Reachability of Carbon Reduction Targets of 13th Five-Year Work Plan in Shanxi Province
3.3. AnalysisonReachabilityofCarbonReductionTargetsof13thFive-YearWorkPlaninShanxiProvince
As already stated, according to the ‘13th Five-Year Work Plan for Controlling Greenhouse Gas
Asalreadystated,accordingtothe‘13thFive-YearWorkPlanforControllingGreenhouseGas
Emissions’, Shanxi Province has had a target of carbon emissions reduction that translates to a carbon
Emissions’,ShanxiProvincehashadatargetofcarbonemissionsreductionthattranslatestoacarbon
intensity reduction of 18% by 2020. We now estimate whether the carbon emissions reduction target
intiesn ascihtyierveadbulec tuiosinngo fth18e %folbloyw20in2g0 .foWrmenuolaw. Qe sdtiemnoatteesw thhee rtheaecrhtahbeilciatyr boof ntheem ciasrsbioonn sreredduuctcitoinon tatragregtest is
achoife ‘v13atbhl eFuivsei-nYgetahr eWfoolrlko wPlianng’ ifno rSmhaunlax.i PQrodveinnoctee. sthereachabilityofthecarbonreductiontargetsof
‘13thFive-YearWorkPlan’inShanxiProvince.
ˆ
CI CI
QQ==CI202105C15−I CˆI22002200×110000%% (3)(3)
CI 2015
2015
AcAcoccrodridngintgo tthoe t‘1h3et h‘1F3itvhe -YFeivaer-WYeoarrk PWlaonrk’o fPSlahna’n xoif PSrohvainnxcie ,PthroevSinhcaen,x ithPer ovShinacnexDi ePvreolvoipnmcee nt
anDdeRveefloorpmmeCnotm anmdi sRseiofonrm‘13 CthomFimveis-Ysieoanr ‘W13othrk FPivlaen-Y’eaanrd Wdoervke lPolpanm’ eanntdp droegvrealomp,mthenetS phraongxriamPr,o tvhien ce
enSehrgaynxdi ePvreolvoipncme eennterpglya ndneivneglo,pamndenott phlearn‘n1i3ntgh, aFnivde o-Ytheearr ’‘1c3othn tFroivlet-aYregaert’s c,oXn0trotol tXar4gpetrse, dXic0 ttioo nXs4 of
ShparnexdiicPtrioonvsi nocfe Sharaenxais Pfroolvloinwces .arXe as= f1o6ll,o1w54s.. 7Xy0 u= a1n6/,1p5e4r.7s oynu,anX/pe=rs3o8n.,2 X5%1 =, 3X8.2=5%1,. 1X52, =X 1.1=5, 4X430 =. 51,
0 1 2 3
X =561.78billionyuan;inputthemintoEquation(2),theresultisCˆI =^3.955t/10,000yuan.Table1
4440.51, X4 = 561.78 billion yuan; input them into Equation (2), the resu2l0t2 i0s CI2020 = 3.955 t/10,000 yuan.
showstheactualcarbonintensityin2015as4.87(t/100,000yuan);wheninputitintoEquation(3),the
Table 1 shows the actual carbon intensity in 2015 as 4.87 (t/100,000 yuan); when input it into Equation
resultisQ=18.78%.
(3), the result is Q = 18.78%.
Asweforecast,thecarbonintensityofShanxiProvincein2020wouldbeabout18.78%lessthan
As we forecast, the carbon intensity of Shanxi Province in 2020 would be about 18.78% less than
thathtaint i2n0 1250.15T.h Tishirse sruesltuilnt dinicdaicteastetsh tahtatth tehtea rtagregteotf ocfa crabrobnonre rdeudcuticotino,n1, 81%8%d edcelaclraerdedin inth tehen enwewfi vfiev-ey-ear
(i.ey.e,athr e(i‘.e1.3, tthh-eF ‘i1v3et-hY-eFaivreW-Yoerakr PWlaonrk’) Pwlailnl’b) ewailclh biee vaechdi.eved.
3.4. EstimationofCarbonEmissionsReductionPotentialinShanxiProvinceduringthe13thFive-Year
3.4. Estimation of Carbon Emissions Reduction Potential in Shanxi Province during the 13th Five-Year
WorkPlan
Work Plan
Theaboveassessmentshowsthatthecarbonemissionsreductiontargetsinthe‘13thFive-Year
The above assessment shows that the carbon emissions reduction targets in the ‘13th Five-Year
WorkPlan’forShanxiProvincecanbeachieved. However,thereappearstoberoomforadditional
Work Plan’ for Shanxi Province can be achieved. However, there appears to be room for additional
redreudcuticotnio;nw; ewneo nwowus uesteh tehGe AGAto toso slovleveth tihsi.sA. Aftfeterrt atakkininggi nintotoa accccoouunntt tthhee eenneerrggyy ddeevveellooppmmeenntt ttrreenndd in
Shiann SxhiaPnroxiv Pinrcoev,inadceo,p atdinogpttihneg stuheg gsuesgtgioenstsioonfsa ouft hauorthitoiersitiiensv ionlvvoeldveadn dantdar tgaertgsestse tseint itnh eth‘e1 3‘1th3thFi Fviev-eY-ear