Impact of Firms Observation Network on the Carbon Market

Transcription

1 energes Artcle Impact of Frms Observaton Network on the Carbon Market Song-mn Yu 1 ID and Le Zhu 2, * 1 Insttute of Scence and Development, Chnese Academy of Scences, Bejng , Chna; yusongmn@caspm.ac.cn 2 School of Economcs & Management, Behang Unversty, Bejng , Chna * Correspondence: lezhu@buaa.edu.cn; Tel.: Receved: 13 June 2017; Accepted: 13 July 2017; Publshed: 8 August 2017 Abstract: Gven the mportant role of the carbon market n fghtng aganst global warmng, the mpact of nformaton on the effcency of the scheme s a crtcal ssue for both ts desgners and the central authorty. At least two aspects of nformaton are worthy of attenton. Frst s the ncompleteness of nformaton, wth whch the frms make decsons to mnmze ther abatement cost and maxmze ther proft. Second s the mechansm of nformaton transmsson. Based on an agent-based framework we establshed n our prevous work, we explctly depcted the frst aspect and analysed ts mpact on frms decson-makng and consequent market results. In ths paper, we focus on the second aspect, transmsson mechansm of nformaton, whch s depcted as an observaton network among frms. The bass n realty s that the frms n the carbon market are usually from dfferent ndustres or areas, and t s relatvely easer to observe the condtons of frms from the same ndustry or area, correspondng to neghbours n the network. Four scenaros are consdered, ncludng no network, regular network, random network, and small-world network. We fnd that the exstence of an observaton network has a sgnfcant nfluence on the market results. Keywords: carbon market; agent-based model; ncomplete nformaton; observaton network 1. Introducton Facng the challenge of global warmng and the pressure of carbon emsson reducton, the scheme of emsson tradng has been regarded as a flagshp polcy nstrument. Numerous studes have been conducted on the desgn [1,2] of ths scheme. Addtonally, the mpact of the scheme has also been evaluated, ncludng s mpact on the macro economy [3 5], economc performance of sectors or frms [6,7], and fnancal performance of frms [8,9]. However, several aspects of complexty have pushed tradtonal equlbrum-based models to ther lmt, ncludng the dynamc aspect of market evoluton, bounded ratonalty, heterogenety, and ncomplete nformaton of the frms n the market. So, n our prevous work [10], we establsh an agent-based framework for the smulaton of the carbon market, consderng the four aspects of complexty above. Wthn ths framework, frms ncomplete nformaton s explctly modelled as an attrbute Eyesght of frms (EYE ), representng the lmted number of other frms that a frm can observe when formng ts forecast of the allowance prce. Then, we analyse ts mpact on frms decson-makng and consequent market results. We fnd that there s a statstcally sgnfcant mpact of frms EYE on the market results: when EYE rses, the average allowance prce rses, and frms total adoptons of low-carbon technologes decreases. Furthermore, we fnd that the socal average abatement cost decreases wth rsng EYE, whch ndcates a hgher effcency of the carbon emsson tradng scheme. Ths s because wth more nformaton, more neffcent adoptons of low-carbon technologes are avoded. Apart from the ncompleteness of nformaton, another aspect worthy of attenton s the transmsson mechansm, whch s the man focus of ths paper. It s depcted as an observaton Energes 2017, 10, 1164; do: /en

2 Energes 2017, 10, of 14 network among frms. The bass n realty s that the frms n the carbon market are usually from dfferent ndustres or areas, and t s easer to observe the condtons of frms from the same ndustry or area. These condtons nclude producton quantty, energy ntensty, adopton of low-carbon technologes, etc. Neghbourng frms n the observaton network correspond to frms from the same ndustry or area. As far as we know, no study has yet analysed the nfluence of frms observaton network on the carbon market, nether theoretcally nor emprcally. However, n a broader vew of network scence, the nfluence of the observaton or nteracton network among agents has been a common ssue n the studes of fnancal markets [11 13] and technology dffuson processes [14 16]. Correspondngly, the agent-based model s one of the major tools for a relevant analyss. Both of these strands of studes provde referental sgnfcance. In ths paper, we attempt to smulate the nfluence of frms observaton network n the carbon market based on an agent-based model. In our model, the ntroducton of observaton s related to the formaton of frms forecasts of the allowance prce. Frms form ther forecast based on two aspects of nformaton. Frst, a frm (e.g., frm ) forms ts fundamental estmate of the allowance prce based on condtons of EYE neghbour frms that t can observe. By observng the other EYE frms condton, frm can calculate a fundamental estmate of the allowance prce from the equlbrum perspectve based on three aspects of nformaton. The frst s expected total emsson of these EYE frms, whch s calculated based on ther hstorcal emsson, total producton n the rest perods of the abatement phase, and current energy ntensty. The second s current holdngs of emsson allowance of these EYE frms. The thrd s the margnal abatement cost curve (MACC) of these EYE frms, whch s calculated based on ther avalable low-carbon technologes, and the calculaton method s ntroduced n Secton 2.2. The total allowance gap of these EYE frms can be calculated based on the frst two aspects of nformaton. Then, the fundamental estmate of the allowance prce can be calculated wth MACC combned. Second, frm calculates the movng average of the allowance prce, whch represents the techncal aspect of nformaton. Fnally, frm s forecast of the allowance prce s a combnaton of both fundamental and techncal nformaton. Ths forecast serves as a unform benchmark for frm to coordnate ts multple abatement-orented decsons, to mnmze ts abatement cost and maxmze ts proft. As a result, the exstence of an observaton network among frms can nfluence frms forecasts of the allowance prce through fundamental aspects of nformaton, as well as ther abatement-orented decsons and the consequent market results. Regardng the observaton network, four scenaros are consdered n ths paper, ncludng no network, regular network, random network, and small-world network. The reason for choosng these four knds of network as scenaros s that ther average EYE can be controlled equal, whch guarantees the comparablty among dfferent networks. The no network scenaro serves as the base scenaro n the analyss. Based on the smulaton results, we fnd that the exstence of an observaton network has a sgnfcant nfluence on the market results, ncludng the allowance prce and tradng volume n the carbon market, product prce and producton n the output market, and frms total adopton of low-carbon technologes. Whle there s no sgnfcant dfference among the three scenaros wth the observaton network, above all, when the observaton network exsts, the socal average abatement cost ncreases wth frms average EYE rsng, whch ndcates an effcency loss wth ncreasng nformaton. Ths s because the exstence of an observaton network among frms delays the transmsson of nformaton, whch leads to hgher revenue loss n the output market when the average EYE of frms rses. Ths paper s structured as follows: Secton 2 presents a bref ntroducton of our model. A detaled ntroducton can be found n our prevous work [10]. Secton 3 ntroduces settngs of the smulaton scenaros, and smulaton results are shown and dscussed n Secton 4. Fnally, conclusons are gven n Secton 5.

3 Energes 2017, 10, of Model 2.1. Model Structure Ths model smulates an abatement phase wth T perods and each perod s denoted by t. There are NA agents, representng the target frms, n the carbon market, who compete wth each other n both of the output and the carbon markets. All frms share a common goal, whch s to maxmze ther total proft. However, gven the abatement pressure, frms need to coordnate three abatement-orented optons, ncludng producton adjustment, low-carbon technology adopton, and allowance tradng. By the end of the abatement phase, frms total emssons of CO 2 are verfed, and those who emt more than the holdngs of allowance must pay a hgh fne for each ton of excess emsson. In order to characterze ths whole process, we ntroduce a complex tme frame n ths model. As shown n Fgure 1, n each perod t, correspondng to a day n the real world, agents frst make ther decsons of producton quantty, whch s recorded together wth the amount of nduced emsson of CO 2 under ther condton of energy ntensty. The prce of the output s calculated based on a lnear market demand curve, whch s P p t = A B =1 NA q t. Then, the agents go through the forecast process and form ther forecast of the allowance prce, whch serves as a unform benchmark for agents to coordnate three abatement-orented optons, ncludng producton adjustment, low-carbon technology adopton, and allowance tradng. The tradng process n the carbon market s organzed based on a contnuous double aucton mechansm. Fgure 1. Model structure Low-Carbon Technology and Margnal Abatement Cost Curve There are NT low-carbon technologes consdered n ths model. Each of them s modelled as three parameters, ncludng adopton cost coeffcent (IC j ), operatng and mantenance cost coeffcent (OMC j ), and energy-savng coeffcent (ES j ). However, snce dfferent agents have dfferent atttudes toward rsk (whch are depcted by heterogeneous values of ther payback perod, PB ), the average abatement cost of a technology j for agent s calculated as follows: ac,j t = IC j + PB OMC j ES j P e y=1 (1+r f ) y PB y=1 ES j EF (1) where r f s the rsk-free rate of return, P e s the prce of energy, and EF s the emsson of each unt of energy utlzaton. Furthermore, based on the estmaton of agent s producton n the remanng

4 Energes 2017, 10, of 14 perods of the abatement phase, denoted by Q t, potental abatement of technology j for agent n perod t s calculated as A,j t = ES j Q t EF. Thus, based on the calculaton of ac,j t and A,j t for all avalable low-carbon technology optons of agent, a margnal abatement cost curve (MACC) for agent or a group of agents can be calculated. It s worth notng that unlke the MACCs n prevous work by Baker et al. [17] and Bauman et al. [18], n whch the abatement cost s essentally resulted from producton reducton and factor substtuton, the MACC n our model follows the dea of L and Zhu [19], and t explctly reflects the cost and abatement potental of multple low-carbon technologes from a bottom-up perspectve Agents In ths model, agents can be abstracted as collectons of attrbutes and behavoural functons. The attrbutes nclude ther scale (Q last, represents agent s total producton n the last year), ntal adoptons of low carbon technology (ITA ), payback perod (PB, represents agent s atttude toward rsk; a lower PB ndcates relatvely hgher rsk-averson), eyesght (EYE ), probablty of alterng forecast of allowance (PrF ) or producton quantty (PrA ), etc. Concernng the behavoural functons, each agent has three abatement-orented optons: producton adjustment, low-carbon technology adopton, and allowance tradng. However, n order to coordnate these three optons, agents frst need to form ther forecast of the allowance prce, whch serves as a unform benchmark for comparson Observaton Network and Forecast In each perod, agent has a probablty of PrF of adjustng ts forecast of the allowance prce. As ntroduced n Secton 1, when formng the forecast, agents combne two aspects of nformaton. Frst s the fundamental aspect of nformaton, whch s detaled nformaton of EYE other agents, ncludng ther producton quantty, energy ntensty, hstorcal emssons, allowance n hand, avalable low-carbon technology optons, etc. Second s the techncal aspect of nformaton, whch s the movng average of the allowance prce. The techncal aspect of nformaton s the same for all agents, whle the fundamental aspects of nformaton are dfferent, dependng on how the observaton network s establshed. Four scenaros are consdered n ths paper, ncludng no network, regular network, random network, and small-world network, as shown n Fgure 2. For the no network scenaro, agent randomly observes EYE other agents n each perod t, and forms ts forecast of the allowance prce. Then n perod t + 1, agent forgets ths nformaton, and randomly observes another group of agents, and forms ts forecast of the allowance prce. Thus, n the no network scenaro, by observng condtons of others, the agents get lmted but global nformaton (n the no network scenaro, though agent can only observe a lmted number of other agents n each perod t; however, snce all other agents can be randomly observed by agent, we say agent has lmted but global nformaton), whle n the other three scenaros, the observaton network s set before the abatement phase, and the observaton relatonshp between two agents s mutual and constant. Thus, by observng others, the agents n these three scenaros get lmted and local nformaton (compared wth the no network scenaro, snce agent n these three scenaros can only observe a constant group of neghbour agents, we say agent has lmted and local nformaton). The regular network s a knd of network n whch each node has the same degree. Whle n the random network, some lnks are created randomly. The small-world network was ntally ntroduced to descrbe the transton from a regular network to a random network. The generatng algorthm of three networks can be found n the work by Albert and Barabás [20], and Watts and Strogatz [21]. The comparablty among the four networks s establshed on the bass of a common average degree of nodes;.e., the average EYE of agents n the network.

5 Energes 2017, 10, of 14 (a) no network network (a)(a) nono network regular regular network network (c) random network (c) (c) random random network network (b) regular network (b)regular regularnetwork network (b) (c)random randomnetwo netw (c) (d) small-world network (d) (d)small-world small-worldnetwork network Fgure 2. Four observaton networks Abatement-Orented Decsons Based on the forecast of the allowance prce, agent coordnates ts three abatement-orented optons by makng three decsons: producton adjustment, adopton of low-carbon technologes, and allowance tradng n the carbon market. However, snce hundreds or even thousands of agents have to coordnate these three optons along a dynamc process whch contans great uncertanty, ther behavours would nevtably reflect propertes of bounded ratonalty, as they lack suffcent tme and nformaton to make decsons. Thus, followng the work by Ggerenzer [22], we assume that agents make decsons based on a seres of fast and frugal heurstcs;.e., rules of thumb (for smplcty, here we provde a bref ntroducton of these rules, and a detaled ntroducton of each rule can be found n our prevous work [10]). 1. Producton Adjustment Decson In each perod, agent has a probablty of PrA of adjustng ts daly producton. We ntroduce a probablty ( βt ) to characterze agent s propensty to adjust ts producton coeffcent (θt ) (Agent s daly producton s qt = θt Qlast /T, and θ0 s set to 1, ndcatng that agent s ntal daly producton was equal to ts average daly producton n the last year. Then, durng the abatement phase, when agent wants to reduce ts producton, we let θt +1 = θt δ. In turn, when agent decdes to ncrease ts producton, we let θt +1 = θt + δ. Here, δ s an exogenous parameter.). When agent s proft of unt emsson from the output market (cpat ) (Here the emsson allowance s treated as a producton factor. Suppose that the product p prce s Pt per ton, and the producton cost s c per ton. The energy ntensty of agent of )the energy s EF. Then, proft of unt emsson for agent s s ρt. The emsson factor ( ) ( p calculated as cpat = Pt c / ρt EF.) s hgher than a benchmark prce (bmpt ) (Here, bmpt s a benchmark prce for agent to make producton decson, and t s calculated as

6 Energes 2017, 10, of 14 bmp t = p f m t pro1. prok (k = 1, 2, 3) are agent s three behavoural parameters related to ts producton decson. A detaled explanaton of the behavoural parameters can be found n the Appendx of [10].), t mght ncrease ts producton, and the probablty s calculated as β t = (cpa t /bmp t 1 ) pro 2. When agent s proft of unt emsson from the output market (cpa t ) s lower than a benchmark prce (bmp t ), t mght decrease ts producton and the probablty s calculated as β t = (bmp t /cpa t 1 ) pro Low-Carbon Technology Adopton Decson For smplcty, we assume that n each perod t, agent only consders the adopton of the avalable low-carbon technology wth the lowest average abatement cost. A probablty (γt ) s used to characterze ts propensty to adopt ths technology. Reasonably, we assume that γt s related to four factors: (1) bac t, whch s the average abatement cost of the technology beng consdered, and we assume γt / bac t < 0; (2) bea t, whch s the potental emsson abatement of the technology n the remanng perods of the whole abatement phase, and we assume γt / bea t > 0; (3) ena t, whch s agent s expected net allowance, whch s ts holdngs of allowance mnus the expected total emsson n the whole abatement phase, and we assume γt / ena t < 0; and (4) bmt (lke bmp t, bm t s a benchmark prce for agent to make producton decson, and t s calculated as bmt = p f l t nv1 ), whch s the benchmark prce for agent to compare wth when makng adopton decsons, and we assume γt / bm t > 0. On the other hand, as a probablty, the value doman of γt s [0, 1]. Thus, based on the four factors lsted above and ther relatonshp wth γt, we ntroduce a modfed sgmod functon for the calculaton of γt as follows (here nvk (k = 1, 2, 3, 4, 5, 6, 7) are agent s seven behavoural parameters related to ts adopton decson; among them, nv 2, nv4, and nv6 are larger than 1). nv 3 nv 5 nv 7 γt = ( ) bac 1 + nv 2 t /bmt ( ) ena 1 + nv 4 t ( ) bea 1 + nv 6 t (2) 3. Allowance Tradng Decson In ths model, there are NK tcks n each perod, each of whch s denoted by s. On each tck, an agent s randomly selected to trade wth other agents n the carbon market. The tradng process s organzed based on the contnuous double-aucton mechansm. Once selected, an agent frst consders whether to trade as a buyer or a seller. Then, t wll submt a bd or ask order to the order book. The order s a combnaton of a lmt prce POt,s and a lmt volume VOt,s, meanng that agent would lke to sell (or buy) VO t,s unts of allowance at a prce no lower (or hgher) than POt,s. Followng the work by Raberto and Cncott [23], we let agent decdes ts lmt prce and volume randomly accordng to the current allowance prce and ts allowance gap (a detaled ntroducton can be found n our prevous work [10]). Addtonally, concernng agent s decson of tradng drecton, we ntroduce a probablty (ξt,s ) to characterze ts propensty to trade as a buyer or seller, and t s calculated as follows. When the current prce of allowance (Pt,s a ) s hgher than the benchmark prce (bmt t,s ) (The calculaton of bmt t,s s dfferent from bmp t,s and bm t,s for contanng the nfluence of agent s net allowance (enat t,s ), the dfference between holdngs of allowance and expected total emsson n the whole abatement phase. Here we reasonably assume that the hgher (or lower) enat t,s s, the lower (or hgher) agent s bmt t,s s, and the hgher propensty for agent to trade as a seller. Because gven other factors equal, when agent s enat t,s s hgh, t faces a lower probablty of beng fned by the end of the abatement phase, and a hgher probablty of losng the value of excess allowance n hands. In order to characterze ths mpact of enat t,s on bmt t,s, we also ntroduce a modfed sgmod functon as follows: bmt t,s = p f m t

7 Energes 2017, 10, of 14 { { [ ( / 1 + ( )] tra 1 ) enat tra 2 }} t,s 0.5.), agents mght trade as a seller, and the probablty s calculated as ξ t,s = (P a t,s /bm t 1) tra 3. When the current prce of allowance (P a t,s ) s lower than the benchmark prce (bmt t,s ), agents mght trade as a buyer, and the probablty ) tra s calculated as ξt,s (bmt = t,s/pt,s a 4 1 (here, tra k (k = 1, 2, 3, 4) are agent s four behavoural parameters related to ts allowance tradng decson). Ths probablstc framework also follows the dea of Raberto and Cncott [23], but the ntroducton of bmt t,s reflects the abatement motvaton of agents allowance tradng behavours. 3. Smulaton Settngs For the smulaton, we assume the whole abatement phase ncludes 365 perods, and each perod ncludes 240 tcks. There are 100 frms competng wth each other n ths vrtual world, and they are ordered and numbered ( = 1, 2, 3,..., 100) accordng to ther scale. All the attrbutes of agents, parameters of market demand, and coeffcents of low-carbon technologes are the same as the settngs of the base scenaro n our prevous work [10] (there s a complete ntroducton to all the attrbutes, parameters, and coeffcents, as well as ther value settngs for smulaton n our prevous work [10]; however, snce most of them are not related to the dscusson n ths paper, they are omtted n ths paper for smplcty). The only dfference s the settngs of the observaton network among agents. As ntroduced n Secton 2.3.1, four knds of networks are consdered n ths paper, ncludng no network, regular network, random network, and small-world network. For each knd of network, the average EYE of agents ranges from 10 to 40 wth an nterval of fve. For the no network scenaro, each agent (e.g., agent ) randomly observe EYE other agents n each perod t when formng ts forecast of the allowance prce. However, for the other three scenaros, the observaton network among agents s set before the abatement phase. Each agent, say agent, observes EYE neghbour agents, and the observaton relatonshp between two agents s mutual and constant. The no network scenaro serves as the base scenaro n the analyss. 4. Results and Dscusson In ths secton, the smulaton results of our model are organzed n three parts to gve a heurstc explanaton of the mpact of the observaton network among frms. Frst s ts mpact on allowance prce and low-carbon technology adopton. Second s ts mpact on allowance tradng volume and total producton. Thrd s ts mpact on the effcency of the carbon emsson tradng mechansm Allowance Prce and Low-Carbon Technology Adopton As ntroduced n Secton 2.3.1, the agents n the no network scenaro obtan lmted but global nformaton, whle the agents n the other three scenaros obtan lmted and local nformaton. As a result, when an neffcent adopton of low-carbon technology occurs, t s easer to be observed by more other agents n the no network scenaro than n the other three scenaros. The agents who observe the neffcent adoptons reduce ther forecast of the allowance prce mmedately, whch then leads to a declne of the real allowance prce. Thus, more neffcent adoptons of low-carbon technologes can be avoded n the no network scenaro than n the other three scenaros. The more neffcent adoptons that are avoded, the tghter the allowance supply s, and the hgher the average allowance prce s. These results are shown n Fgure 3. In Fgure 3, the red lnes represent the allowance prce n the carbon market, and the lght red areas represent the varaton n 30 smulatons. The four horzontal dotted blue lnes represent the total technology adopton n each of the scenaros. The no network scenaro has the least adoptons of low-carbon technologes, whch means that the most neffcent adoptons are avoded. Addtonally, as a result of more neffcent adoptons of low-carbon technologes, the allowance

8 Energes 2017, 10, of 14 prce falls to zero n the other three scenaros. However, these three scenaros do not exhbt sgnfcant dfferences. For a smlar reason, the ncrease of EYE ndcates more nformaton for agents to make decsons, whch also leads to less neffcent adoptons and a hgher average allowance prce. The no network scenaro follows dfferent expanson paths from the other three scenaros when EYE rses (as shown n Fgure 4), whle the lnes for the other three scenaros entangle wth each other, whch ndcates lttle dfference among them. (a) no network (b) regular network (c) random network (d) small-world network Fgure 3. Allowance prce and low-carbon technology adopton when EYE s equal to Allowance Tradng Volume and Total Producton Concernng the mpact of the network structure on the tradng volume of allowance and total producton, we also choose the smulaton results for four scenaros wth the agents average EYE equal to 15 as an example. The relevant results are shown n Fgure 5. In Fgure 5, we can see that for the no network scenaro, frms total producton represented by the dotted blue lne s sgnfcantly hgher than that n the other three scenaros. Ths s the same for the total tradng volume of allowance. As ntroduced n Secton 4.1, ths s because frms have lmted but global nformaton n the no network scenaro, and neffcent adoptons of low-carbon technologes are observed by more agents, whch leads to a faster declne of both agents forecasts and real allowance prce compared wth n the other three scenaros. Addtonally, frms can also adjust ther producton faster, as shown n Fgure 6.

9 Energes 2017, 10, of 14 (a) average prce (b) technology adopton Fgure 4. Expanson paths of average allowance prce and technology adopton. (a) no network (b) regular network (c) random network (d) small-world network Fgure 5. Allowance tradng volume and total producton when EYE s equal to 15.

10 Energes 2017, 10, of 14 (a) no network (b) regular network (c) random network (d) small-world network Fgure 6. Producton when EYE s equal to 15. At the same tme, the hgher total producton of agents also leads to a hgher tradng volume of allowance n the carbon market, as shown n Fgure 5. Ths s because for the frms wth hgh abatement costs by low-carbon technology adopton, the more frms produce, the greater the allowance gap s, and the hgher the tradng volume of allowance s n the carbon market. On the other hand, as dscussed n Secton 4.1, when frms average EYE rses, more neffcent adoptons of low-carbon technologes can be avoded, whch also means stronger pressure for frms to reduce ther producton for abatement. However, as a result of the exstence of an observaton network, frms total producton s further hgher n the no network scenaro than n the other three scenaros. Ths s because although the stronger pressure leads to a sharper reducton of producton at the begnnng of the abatement phase, the agents n the no network scenaro can adjust ther producton faster when they observe the neffcent adoptons of low-carbon technologes, whle the agents n the other three scenaros cannot. A comparson between the two examples wth average EYE equal to 15 and 35 s shown n Fgure 7. As a result, the expanson paths of frms total tradng volume and total producton n four scenaros are dfferent: for the no network scenaro, frms total tradng volume of allowance ncreases wth average EYE rsng, as well as frms total producton; whle for the other three scenaros, frms total tradng volume of allowance decreases wth average EYE rsng, as well as frms total producton. Ths s shown n Fgure 8.

11 Energes 2017, 10, of 14 (a) no network (b) regular network (c) random network (d) small-world network Fgure 7. Producton comparson for EYE equallng 15 and 35. (a) total allowance volume (b) total producton Fgure 8. Expanson paths of total tradng volume of allowance and total producton Effcency of Carbon Emsson Tradng Mechansm In our prevous work [10], we found that the effcency of the carbon emsson tradng mechansm ncreases wth frms rsng EYE, whch s represented by the declne of the socal

12 Energes 2017, 10, of 14 average abatement cost. However, as dscussed n Secton 4.2, when the observaton network exsts, frms total producton decreases wth rsng EYE, whch means a hgher abatement cost of revenue loss n the output market. As a result, the exstence of the observaton network among frms reverses the trend of the socal average abatement cost wth frms rsng EYE, as shown n Fgure 9. Fgure 9. Expanson paths of socal average abatement cost. 5. Conclusons Based on the agent-based model we establshed n our prevous work [10], we attempt to smulate the mpact of the nformaton transmsson mechansm on the carbon emsson tradng scheme. The transmsson mechansm s depcted as an observaton network among frms. Four scenaros are consdered, ncludng no network, regular network, random network, and small-world network. The smulaton results are organzed as three parts. Frst, the mpact of dfferent networks on the allowance prce and frms adopton of low-carbon technologes are analysed. We fnd that wth lmted but global nformaton, more neffcent adoptons of low-carbon technologes are avoded n the no network scenaro than n the other three scenaros. Ths leads to lower total adopton of low-carbon technologes, and hgher average allowance prce. Addtonally, when EYE rses (whch ndcates more nformaton for frms to make decsons), both frms total adoptons and average allowance prce rse n all four scenaros. However, the no network scenaro follows a dfferent expanson path, and there s no obvous dstncton among the other three scenaros. Second, the mpact of dfferent networks on the tradng volume of allowance and frms total producton are analysed. We fnd that the exstence of the observaton network delays the transmsson of nformaton, and also delays frms adjustments of producton n the output market. Ths leads to lower total producton of frms, as well as a hgher revenue loss as the socal cost of abatement. Thrd, followng the second result, our model shows that the exstence reverses the trend of the socal average abatement cost wth the ncrease of frms EYE, snce the ncrease of frms average EYE leads to more abatement cost from revenue loss n the output market. In concluson, the exstence of the observaton network shows a sgnfcant mpact on the carbon emsson tradng scheme n our model. The most mportant reason s that when the network exsts, t delays the global transmsson of fundamental nformaton, whch leads to more neffcent adoptons of low-carbon technology and more revenue loss n the output market. In other words, the exstence of the observaton network reduces the effcency of the carbon

13 Energes 2017, 10, of 14 emsson tradng scheme. In fact, the no network scenaro we set does not exst n the real world. However, the results and dscussons above mplcate the necessty of establshng a publc mechansm of nformaton dsclosure to offset the negatve effect of a network that does exst n the real world. Accordng to the results of our smulaton, a dynamcally updated lst of frms adopton of low-carbon technologes would help to mprove the effcency of the carbon emsson tradng scheme. Fnally, several lmtatons of ths model need to be clarfed. Frst, the frms n the model only refers to frms wth abatement targets n the carbon market, and the fnancal agents n the real carbon market are not yet ncluded. Second, as a theoretcal smulaton model, the agent-based model foremost serves as a heurstc devce for understandng the evoluton of a complex system, scenaro analyss, and polcy evaluaton. In our future work, more work wll be done on calbraton based on real data and emprcal tests of the results of the model. Thrd, the mpact of the carbon emsson tradng scheme also reles heavly on the allocaton mechansm of the allowance. In ths paper, the allowance s allocated based on a grandfatherng mechansm, so frms allocatons are not related to ther real producton. Thus, frms have stronger ncentve to reduce ther producton for abatement, whch shares the same vew wth Fscher and Fox [24]. In our future work, more allocaton mechansms wll be analysed. Acknowledgments: The fnancal support from the Natonal Natural Scence Foundaton of Chna under grant No , No and No s hghly acknowledged. The authors apprecate the weekly semnars at CEEP n CAS, from where the earler draft of the paper got mproved. The authors apprecate the valuable comments and gudance from Yng Fan and Wolfgang Echhammer. Author Contrbutons: Song-mn Yu made substantal contrbutons to the desgn, analyss and draftng of the work. Le Zhu made substantal contrbutons to the concepton, analyss and revsng of the work. Both of Song-mn Yu and Le Zhu are responsble for the fnal approval of the verson to be publshed, and agreed to be accountable for all aspects of the work n ensurng that questons related to the accuracy or ntegrty of any part of the work are approprately nvestgated and resolved. Conflcts of Interest: The authors declare no conflct of nterest. References 1. Sumner, J.; Brd, L.; Dobos, H. Carbon taxes: A revew of experence and polcy desgn consderatons. Clm. Polcy 2011, 11, Bakam, I.; Matthews, R.B. Emsson tradng n agrculture: A study of desgn optons usng an agent-based approach. Mtg. Adapt. Strateg. Glob. Chang. 2009, 14, Wu, J.; Fan, Y.; Xa, Y. The Economc Effects of Intal Quota Allocatons on Carbon Emssons Tradng n Chna. Energy J. 2016, 37, Goulder, L.H.; Hafstead, M.A.; Dworsky, M. Impacts of alternatve emssons allowance allocaton methods under a federal cap-and-trade program. J. Envron. Econ. Manag. 2010, 60, Cu, L.B.; Fan, Y.; Zhu, L.; B, Q.H. How wll the emssons tradng scheme save cost for achevng Chna s 2020 carbon ntensty reducton target? Appl. Energy 2014, 136, Damen, D.; Phlppe, Q. European Emsson Tradng Scheme and compettveness: A case study on the ron and steel ndustry. Energy Econ. 2008, 30, Martn, R.; Muûls, M.; Wagner, U.J. The mpact of the European Unon Emssons Tradng Scheme on regulated frms: What s the evdence after ten years? Rev. Envron. Econ. Polcy 2015, 10, Oestrech, A.M.; Tsakas, I. Carbon emssons and stock returns: Evdence from the EU Emssons Tradng Scheme. J. Bank. Fnanc. 2015, 58, Mo, J.L.; Zhu, L.; Fan, Y. The mpact of the EU ETS on the corporate value of European electrcty corporatons. Energy 2012, 45, Yu, S.; Fan, Y.; Zhu, L. How Do Heterogenety and Incomplete Informaton Influence the Carbon Market? An Agent-Based Approach. 2017, Workng Paper. Avalable onlne: project/agent-based-smulaton-for-emsson-tradng-scheme (accessed on 24 May 2017). 11. Zhang, J.; McBurney, P.; Musal, K. Convergence of tradng strateges n contnuous double aucton markets wth boundedly-ratonal networked traders. Rev. Quant. Fnanc. Account. 2017, 1 52, do: /s

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