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شرکت سازنده: DynamicTraders.comنام انگلیسی: Dynamic Traderشماره نسخه: v7.1.2.218نام فارسی: داینامیک تریدرحجم فایل: 12.5 مگابایتتاریخ انتشار: 13:42 - 1399/6/1 | 2020.08.22منبع: پی سی دانلود / www.p30download.irامتیاز: 4/5

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This study employs a laboratory experiment to assess the performance of tradable permit markets on dynamic efficiency arising from cost-reducing investment. The permit allocation rule is the main treatment variable, with permits being fully auctioned or grandfathered. The experimental results show significant investment under both allocation rules in the presence of ex ante uncertainty over the actual investment outcome. However, auctioning permits generally provides stronger incentives to invest in R&D, leading to greater dynamic efficiency compared to grandfathering.

Emissions trading schemes are increasingly being employed across the globe as a prominent market-based environmental policy instrument to curtail emissions cost-effectively. Competitive emissions trading programs can equalize marginal abatement costs (MAC) across firms so that the abatement target is reached efficiently. Alongside this general static efficiency result, however, is sparse evidence concerning the incentives that tradable permit markets create for investment in the development of advanced abatement technology. Our study aims to fill this gap by providing experimental evidence on such investment behaviour, providing some initial empirical insight into the dynamic efficiency performance of tradable permit markets.

The allocation mechanism is a key issue in view of design, implementation and potential (dynamic) efficiency of many different types of markets, ranging from rights-based fisheries management (Anderson et al. 2011) to emissions trading (Lyon 1982; Goeree et al. 2010; Grimm and Ilieva 2013). For instance, the European Union Emissions Trading System (EU ETS) is currently transitioning from mostly grandfathered to mostly auctioned permits. Botelho et al. (2011) study the EU ETS market institution in the laboratory by comparing the performance under a full auctioning and full grandfathering rule, and they provide experimental evidence showing that total abatement costs are similar under both allocation mechanisms. This finding confirms the standard static efficiency result mentioned above. Moreover, their study also shows that auctioning can allocate permits more accurately across sources. This allows sources to reach the minimum abatement cost solution more effectively because less post-allocation permit trade between sources is required.

Closer to our study, Camacho-Cuena et al. (2011) compare the adoption incentives of advanced abatement technology in an emissions trading experiment under permit auctioning and grandfathering. Their results reveal the absence of an adoption differential and show that both auctioning and grandfathering are equivalent for static and dynamic efficiency. More recently, Taschini et al. (2014) explore the timing of (irreversible) adoption decisions of abatement technology in the laboratory under a grandfathering allocation rule only. The authors build in abatement cost uncertainty by assuming that emissions are stochastic. In such a setting, Taschini et al. find that firms tend to invest in the adoption of abatement technology faster when a strict enforcement mechanism is in place that penalizes firms when facing emissions in excess of permit holdings. Suter et al. (2013) also focus on adoption of existing technology, but in a context of water quality trading markets featuring a limited number of firms, and they show that traders tend to over-invest in technology that requires (large) capital investments. This over-investment holds especially for those traders who experience more restricted abatement opportunities due to their use of high-cost abatement technology. Over-investment is also found in Gangadharan et al. (2013), who examine the role of banking in conjunction with opportunities to invest in abatement technology and uncertainty about the future permit allocation. In a tradable permit market experiment based on a grandfathering allocation rule, one of their key findings is that in situations where both banking and investment is allowed, the degree of over-banking is reduced relative to a treatment with only the option to bank.

Our paper extends the above research by concentrating on the interaction between the permit allocation mechanism and the R&D investment decisions by explicitly acknowledging the stochastic nature of R&D investment rather than stochastic emissions as in Taschini et al. (2014). In our experimental analysis we compare the economic gains from actual investment levels to the gains from R&D choices that lower abatement costs efficiently by optimally trading off marginal investment costs and marginal impacts on abatement and permit prices. Like Suter et al. (2013) and Gangadharan et al. (2013), traders over-invest in new technology, and this occurs in both allocation mechanism treatments. Contrary to the study of Camacho-Cuena et al. (2011) on technology adoption, in our experimental permit market with stochastic R&D we find that auctioning results in significantly greater R&D investment compared to grandfathering.

We use a two-stage model, since this is the simplest environment in which dynamic effects arise. This two-stage approach allows for a much simpler experimental design, compared to an alternative designed to mimic an infinite horizon. It also allows for more straightforward data analysis, since the data are always organized as two-stage blocks (Agranov et al. 2016). This approach seems particularly suited for an experiment designed to identify and compare such dynamic effects from investment across treatments. In particular, each single trading period is divided into two trading Stages: I and II. The length of the period and the different trading Stages is common knowledge amongst traders. Figure 2 summarizes these two stages as well as the timing of the above outlined events within a single period. Let us outline these events in detail.

At the start of trading Stage I, fixed revenues and permits are distributed to traders. Depending on the treatment, the distribution of permits occurs via either grandfathering or the allocation auction. In the G-treatment 2 permits are allocated to type H traders and 6 permits to the type L traders. This asymmetric initial allocation induces the high-cost type to be (net) permit buyers and the low-cost type to be (net) permit sellers in equilibrium. To equalize MAC across firm types, in equilibrium each firm would need to buy (if type H) or sell (if type L) five permits. In contrast to the G-treatment, in the A-treatment we do not fix the specific permit endowment but traders can buy any number of permits subject to the constraint that the aggregate permit supply cannot exceed 32. Following the same auction format as is applied in the EU ETS, permits are allocated in a uniform price, sealed-bid auction.Footnote 6 When auctioning off the 32 permits, all permits are sold at a uniform price set by the lowest accepted bid (with any ties broken randomly). Moreover, bidders in the EU ETS auction can place multiple bids. This implies that bidders can enter a bid schedule and are not restricted to bid for a single price-quantity combination. To implement this rule in our allocation auction, traders can only buy up to a maximum of 10 permits, and they can submit a different price for each permit bid they make.

After the permits are allocated in Stage I, traders have the opportunity to buy and sell them in the (continuous) double auction market so as to adjust their permit holdings if they wish. Trading of permits in this reconciliation market lasts for 2 minutes. The double auction market provides a competitive environment where traders are free to submit public offers to purchase and sell permits at a certain price. Throughout the 2-minute transaction time, traders can adjust their offers but new offers must be an improvement over previous offers. That is, any new buy offers must be higher than the current highest buy offer and any new sell offers must be lower than the current lowest sell offer. The equilibrium permit price, \( p^{*} \), is in the range [135,138] given a total supply of 32 permits in the market (see Fig. 1). This is also the relevant equilibrium price range in the A-treatment. However, the equilibrium trading volume is conditional on who acquired the permits in the allocation auction.

Upon completion of Stage I, traders enter trading Stage II. The key difference from trading in the first Stage is that traders now have the opportunity to invest in R&D. Before the R&D investment decisions are made, permits are again allocated either on the basis of grandfathering or auctioning, depending on the treatment. This timing of the permit allocation prior to the actual investment decision allows traders to reap the benefits from their investment in the reconciliation market, i.e., the double auction trading opportunity in Stage II (see Fig. 2). 2b1af7f3a8