business.unsw.edu.au

Last Updated 28 August 2014 CRICOS Code 00098G

 

School of Accounting Seminar Series Semester 1, 2015

Estimation Bias and Monitoring in Clean Development Mechanism

Projects

Naomi Soderstrom

University of Melbourne 

Date: Friday March 20 2015 Time: 3.00pm – 4.00pm Venue: ASB 216

 

Business School

School of Accounting

Estimation Bias and Monitoring in Clean Development Mechanism Projects

Hui Chen University of Zurich

Peter Letmathe

RWTH Aachen University

Naomi Soderstrom† University of Melbourne

This draft: February 21, 2015

Abstract: We examine how monitoring reduces incentives of companies to bias forward-looking estimates. These estimates serve as a criterion for admission into a United Nations program that grants tradable carbon emissions permits for carbon-reducing projects in developing countries. Consistent with our expectation, we find that reported rates of return, a key factor for admission to the program, tend to be downwardly biased and are negatively associated with the forecasted carbon reduction. However, monitoring from various sources mitigates some of the distorted incentives and related misreporting. __________________________ *The authors’ emails are hui.chen@business.uzh.ch, peter.letmathe@rwth-aachen.de, and naomiss@unimelb.edu.au. We gratefully acknowledge funding from a Leeds School Sustainability Research Grant. This work has benefitted from input by Gavin Cassar, Tim Gray, and workshop participants at University of Auckland, George Washington University, University of Houston, LaTrobe University, Monash University, University of Melbourne, University of New South Wales, University of Otago, and Peking University. Tony Cao, Angus Hervey, Phillipp Kloeber, Andrew Lin, Kelly Soderstrom, Karin Temperly, and Sven Raak have provided valuable research assistance. †Corresponding author.

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1. Introduction

Companies provide a wide variety of forward-looking information to investors and other

stakeholders. Prior accounting research has examined the incentives and consequences related to

forward-looking information reported by managers, especially in the context of management

forecasts (e.g., Hutton, Miller, and Skinner 2003; Bozanic, Roulstone, and Van Buskirk 2013).

We examine a unique setting of forward-looking information filed by host firms of Clean

Development Mechanism (CDM) projects to gain admission to a United Nations (UN) program

that promotes carbon efficiency in developing countries. To meet UN requirements and obtain

the tradable emissions allowances associated with program membership, these firms have strong

incentives to bias estimated future project returns. In this setting, monitoring plays a particularly

important role in mitigating these incentives.

The CDM is one of the flexible mechanisms available under the Kyoto Protocol.1 If

CDM projects are approved, host firms can earn tradable carbon credits (Certified Emission

Reductions, hereafter, CERs), each of which is equivalent to the reduction of one metric ton of

CO2 and can be used to satisfy obligations flowing from the Kyoto Protocol and European Union

regulations. CDM projects include, for example, the installation of energy-efficient boilers or

investment in rural electricity plants designed to have low carbon emissions. A major factor for

UN approval is that the project must require subsidization through granted CERs to become

financially viable. That is, the project’s estimated return on investment must be lower than a

reasonable benchmark rate.2 The program thus gives companies strong incentives to underreport

estimated project returns to meet the benchmark rate. In this paper, we examine the host firms’

1 Flexible mechanisms refer to the programs designed to reduce greenhouse gas emissions through projects in other countries instead of achieving these reductions through investment in technologies or other initiatives in the host country. 2 The benchmark may be an explicit hurdle rate, or it may be a return relative to other, more carbon-intensive alternatives.

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incentive to downwardly bias the forecasted project internal rate of return (IRR) to qualify for

the UN approval, as well as how monitoring mechanisms at both the country- and project-level

can mitigate this incentive.

Using data on 2,510 projects across eight countries, manually collected from the

applications filed by host firms, we find that firms do manipulate information consistent with

their underlying incentives. In alignment with our expectations, we find that the applying firms’

reported IRRs are negatively associated with the expected emission reductions. (These

reductions equate to CERs expected to be generated from the projects once the projects are

approved.) This indicates a strong link between potential economic benefit and firms’ incentive

to underreport. Increased monitoring appears to mitigate misreporting. At the country level, we

find that countries with characteristics likely associated with higher quality of monitoring,

specifically, those that are richer and more developed, have host firms that are less likely to

underreport. We also find that the quality of monitoring at the project-level impacts reported

IRRs: projects where the auditor is affiliated with a Big Four accounting firm have significantly

higher reported IRRs than other projects.

We also examine the factors that are associated with reported IRRs that are obviously

erroneous. A subset of firms reports that their proposed projects generate negative IRRs. While

some of the firms correctly report these negative IRRs as incalculable, others report a negative

point estimate for the IRR. We find that the firms that erroneously report point estimates for

negative IRR projects tend to come from countries with relatively poor monitoring—those with

lower CO2 emissions and auditors with inadequate experience. This result indicates that

monitoring not only prevents opportunistic behavior but also improves reporting quality.

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Finally, we investigate why some firms report IRRs higher than the benchmark rate.

Under ordinary circumstances, these projects would not be eligible for the CDM program.

However, if firms can demonstrate that there are significant social, political, or other barriers to

implementation, even projects that are financially viable without the CDM program can be

admitted and earn CERs. As expected, we find that projects that include a barrier analysis are

more likely to have a reported IRR higher than the benchmark. Perhaps related to the existence

of barriers for carbon-reduction investments, these projects are more likely to be from countries

with larger carbon footprints. We also find that the auditors previously sanctioned for not

properly monitoring CDM methodologies are less likely to be associated with projects having

IRRs above benchmark. Successful argumentation for admission to the CDM program when the

project is financially viable may require expertise beyond the ability of these auditors, who have

insufficient expertise.

Our study makes three contributions. First, it contributes to research on the managerial

disclosure of forward-looking information. Our setting is differs from those used in other

accounting research in this area. Typically, the accuracy of forward-looking information can be

validated by eventual disclosure of outcomes. The accuracy of management forecasts, for

example, is validated by the final earnings announcement. In our setting, the estimated IRRs

filed by CDM host firms cannot be verified against the future realized value, since the actual

project return is never publicly disclosed. The information reported in our setting is thus more

susceptible to manipulation, and its accuracy depends more on external monitoring.

Our paper also contributes to the research on environmental disclosure by examining the

CDM host firms’ incentives to underreport their projects’ return on investment. Prior research

often examines environmental disclosures in a voluntary disclosure setting (e.g., Clarkson et al.

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2007; Plumlee et al. 2008; Clarkson et al. 2010; Dhaliwal et al. 2011; Griffin and Lont 2013). In

our setting, while firms voluntarily submit projects to gain acceptance into the CDM program,

they must report projected financial viability of their projects once they do so.

Finally, our study is the first to investigate incentives and monitoring factors that are

systematically associated with reported IRRs in CDM projects. Our study differs from the prior

CDM literature by focusing on the institutional structure, the political environment, reporting

incentives, and the role of monitoring. Prior literature has focused on the information included in

applications, such as whether they provide detailed costs versus only the results of calculations,

and descriptive statistics for the required rates of return used for the projects (Schneider 2009).

Other studies provide in-depth case analyses of projects to highlight specific issues (e.g.,

Michaelowa and Purohit 2007). Michaelowa (2007, referenced in Schneider 2009) notes that

some Indian wind power projects have failed to include tax benefits in the calculation of project

financial viability. The insights we gain are not only relevant for CDM and other instruments

targeting climate change but also illuminate the development of accounting standards and

monitoring mechanisms for environmental issues (Cook 2009).

The next section provides further background on the CDM. Following that, we discuss

related accounting issues and provide a literature review. We then provide theoretical and

hypothesis development and describe our empirical method. Subsequent sections describe our

sample and provide our results. The final section concludes.

2. CDM Background

The CDM was developed as a means of achieving carbon emission reductions to satisfy

the requirements of the Kyoto Protocol. CDM facilitates reduction of greenhouse gas emissions

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through investments in green technologies in developing countries. From an economic

perspective, the program helps to lower marginal abatement costs (if emission reduction is less

costly in developing countries) and promotes technology transfer between developed and

developing countries. While the nominal head of the CDM is the Conference of the Parties,

consisting of contracting parties (countries) of the Kyoto treaty, the CDM Executive Board

supervises current processes. Its main tasks are (Umweltbundesamt 2009):

• Approval of new methodologies for baselines and monitoring

• Approval and registration of projects

• Issuance of CERs

• Development and maintenance of the CDM register

• Accreditation of the Designated Operational Entities (DOEs), who audit project

documents

• Development of recommendations to the Conference of the Parties.

The board is supported by several work groups, which suggest enhancements to existing

methodologies and prepare drafts of methodologies for small scale projects and new sectors (e.g.,

afforestation). The CDM Registration & Issuance Team, the Accreditation Panel and the

Assessment Team review project activities and prepare decisions for the Executive Board.3

Designated national authorities represent the contract partners (countries) of the Kyoto

treaty. These authorities, in both host and nonhost countries, must implement a formal process to

3 Historically, the CDM Executive Board has been populated by carbon consultants and officials from credit-buying countries (Lohmann 2009) rather than technical or financial specialists. The board has therefore relied on the work groups, Designated National Authorities, and DOEs to provide input into decision-making. During our sample period, there were no CDM board members with accounting backgrounds. In 2011, one member (Associate Professor Maosheng Duan) had a doctorate in Management. His research has focused on more macroeconomic and market-level issues, however. In 2012, two new members with business degrees were elected to the board.

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approve CDM projects. In addition, they usually provide assistance in the registration process,

such as providing guidelines, setting some benchmarks, etc.

The DOEs are independent organizations that audit all project documents. They also

verify the actual emission reductions. In contrast to traditional auditors, they do not only evaluate

compliance with accounting standards but assess and approve project proposals. This demands

considerable technical and financial expertise. During our sample period, an average of more

than 700 project applications were evaluated each year. The sheer volume of work alone makes it

difficult for the CDM Executive Board to thoroughly analyze each project. The board therefore

must rely on the expertise and monitoring of the operational entities to ensure that it admits only

eligible projects to the program.

Figure 1 depicts the stages in development of a CDM project. Each project must be

documented in a design document. The document provides the basis for the project’s validation

and registration. It includes a description, information about the applied baseline and monitoring

methodologies, duration of the activity and proposed crediting period, calculation of greenhouse-

gas emission reductions, information on environmental impacts, and stakeholder comments.

Each design document must specify an approved methodology for emission reduction. With the

advice of its technical committee, the CDM Executive Board has already approved a variety of

methodologies for different types of projects. Project developers can suggest alternative

methodologies, which must then be approved by the board. Appendices to the design document

provide further information about participants, public funding, financial analysis results, etc.

One of the major purposes of the document is to justify “additionality,” which is the

primary requirement for projects to be approved. Additionality is where “anthropogenic

emissions of greenhouse gases by sources are reduced below those that would have occurred in

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the absence of the registered CDM project activity.”4 Additionality thus includes justification

from both an emissions-reduction and investment standpoint: to be accepted into the program, a

project should need the granted CERs to become financially viable.5 Otherwise, companies

would have economic incentives to pursue the project, and the CERs would be unnecessary to

make it economically attractive. While the additionality requirement justifies the subsidy granted

to host firms, it also provides incentives for companies to understate the financial benefit of their

projects to satisfy the additionality criterion.

Due to the importance of additionality and the difficulty of its demonstration, the CDM

Executive Board has approved a methodological tool for establishing additionality (hereafter

“guidance” or “additionality tool”). The tool was adopted on October 22, 2004 (CDM Executive

Board 2004). The latest version pertaining to our sample (7.0) dates from November 23, 2012

(CDM Executive Board 2012). It directs project developers to identify alternatives to the project

activity and then, depending on the nature of the project, analyze financial aspects of the

proposed project relative to the alternatives or to a benchmark, identify barriers to

implementation, and compare the proposed project to common practice in the relevant sector and

region. Analysis techniques allowed include simple cost analysis, investment comparison

analysis, or benchmark analysis. Use of the tool is voluntary for projects proposing new

methodologies but mandatory for projects using approved methodologies. The tool serves as the

accounting standard, providing methodological guidance to meet additionality requirements.

Officially, the only projects that may be approved without meeting the financial

additionality requirement are those facing significant barriers to investment. The barriers may be

4 Report of the Conference of the Parties serving as the meeting of the Parties to the Kyoto Protocol on its first session 2005, p. 16. 5 An exception is if there are significant barriers (e.g., social or political) to implementing a project. In these cases, even financially beneficial projects can be included in the program and earn CERs.

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geographical—projects may be located in sites that are hard to access. They may also be

financial—projects may have difficulty attracting local investment. When a project faces such a

barrier, it may be approved even when its potential rate of return is higher than the benchmark

rate.

Upon completion of the design document, the project developer submits the project to a

DOE for validation. A letter of approval from the designated authority for the host country (to

ensure that the project contributes to the sustainable development priorities of the country) and

the designated authority of any Annex I country (countries that have agreed to reductions under

the Kyoto Protocol) participating in the project is also required. Questions asked in the course of

validation include the following: Does the design document meet CDM requirements? Is the

methodology appropriate and correctly applied? Is the additionality criterion met? Are all

necessary documents available and complete? If these criteria are met, the DOE validates the

project and sends the approval letters and all documentation to the CDM Executive Board with a

request for registration. If the project is registered, the project developer monitors project activity

to facilitate calculation of emission reductions and provides a monitoring report to a second DOE

for verification (although the second one does not have to be different from the first). The second

DOE uses the monitoring report and information collected during on-site inspections to develop

a verification report. If the activity level documented is deemed satisfactory, it certifies the

claimed reductions. Finally, the Executive Board issues the CERs, and project participants can

sell or use them. Ownership of CERs is registered by the responsible national registries. If one of

the steps during the approval process is not completed, the project developer has the option to

resubmit.

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3. Literature Review

3.1. Reporting of forward-looking information

Management forecasts are voluntary disclosures provided by a firm’s managers about

estimated future earnings or other relevant information. Managers often have incentives to bias

the forecasted earnings because of their equity-based compensation. Nagar et al. (2003) examine

the relation between managers’ voluntary disclosure of estimated future information and their

stock-based incentives and find that managers with more equity-based compensation issue more

frequent forecasts. The higher level of disclosure stems from the manager’s incentive to avoid

having mispriced stock.

Managers may also opportunistically bias forecasted information to achieve personal

gains, though the bias may not always be upward. Aboody and Kasznik (2000), for example, find

that managers issue forecasts with negative news around the time when stock options are being

awarded, aiming to push down their stock price, resulting in a lower strike price for their granted

stock options. Cheng and Lo (2006) and Rogers and Stocken (2005) also find that managers have

incentives to disclose forecasted information with bad news to lower purchase price when they

engage in insider trading.

Unlike our CDM setting, explicit auditing or monitoring rarely exists for voluntary

management forecasts. However, the repeated nature of information disclosure provides

sufficient incentive for the manager to provide truthful information (Stocken 2000). Even in a

single-period setting, subsequent realization of the forecasted earnings helps to guarantee that

managers cannot bias the information too much (Sansing 1992). In our setting, each company

typically participates in only one project, and there is a lack of verification by the market or a

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regulator. This places a larger burden on monitoring mechanisms to reduce managerial

opportunism.

3.2. Carbon emission and other environmental/social disclosures

While CDM projects require measurement and disclosure of carbon reductions, the

setting differs from exploration of carbon-related disclosures in the accounting literature. Most

accounting studies examine the impact of carbon information from a voluntary disclosure

perspective. Stanny (2010) studies the voluntary nature of disclosures within the Carbon

Disclosure Project (CDP). She finds that the number of firms and the types of information

disclosed increase over time. However, even in the most recent year of her sample (2008), while

70% of firms respond to the survey, only 41% disclose emissions, and only 30% disclose their

accounting methodology. Matsumura et al. (2013) employ voluntary disclosures from CDP and

Ceres to investigate the relation between carbon emissions and firm value. They find that

increases in carbon emissions disclosed are associated with decreases in firm market value,

increases in the cost of debt, and decreases in the cost of equity capital. Griffin et al. (2010)

supplement CDP disclosures with a model predicting greenhouse gas disclosures for

nonrespondents. They find a significant association between disclosed and estimated greenhouse

gas emissions information and stock market valuation. They also find a significant stock market

reaction around the date a company discloses new information related to climate change.

A closely related stream of research comprises studies on general environment or social

disclosures. Dhaliwal et al. (2011) examine the voluntary disclosure of corporate social

responsibility (CSR) activities and find that it is associated with generally lower cost of equity

capital and a dedicated institutional investors and analyst coverage. They also show that the

disclosing firms take advantage of such benefits to raise equity capital. Clarkson et al. (2007)

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focus on purely voluntary environmental disclosures and environmental performance measures

using actual toxic emissions and waste management data. They also construct a content analysis

index based on the Global Reporting Initiative sustainability reporting guidelines to capture firm

disclosures related to the commitment to protect the environment. Using these measures, they

find a positive association between environmental performance and the extent of discretionary

environmental disclosures. Plumlee et al. (2010) examine the relationship between the quality of

a firm’s voluntary environmental disclosures and firm value. They explore different dimensions

of the quality of voluntary environmental disclosure and find different components of the

disclosure quality have different associations with future cash flow and cost of equity. Clarkson

et al. (2010) examine the relevance of voluntary environmental disclosures and find that these

disclosures seem to improve the shareholders’ perceptions of the firms’ performance on

environmental issues. They also show that the enhanced firm value related to these disclosures

seems to come through the channel of cash flow prediction. Endrikat et al. (2014) summarize a

variety of arguments about the economic potential of green management practices and show the

conflicting nature of environmental and financial performance.

Our study provides a different view on environmental disclosures. In our setting, carbon-

related disclosures are mandatory (for companies electing to apply to the CDM program). In

addition, many of the host firms are not publicly traded, so the disclosures are not focused on

investors, nor can market mechanisms serve to reduce opportunism.

3.3. CDM

Empirical analyses of CDM projects have focused on the nature of information included

in applications. Schneider (2009) focuses on whether detailed costs have been reported versus

only the result of calculations and provides descriptive statistics for the required rates of return

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used for the projects (Schneider 2009). Michaelowa (2007) and Michaelowa and Purohit (2007)

conduct in-depth case analyses of projects, noting inconsistencies and weaknesses in the analyses

and treatment of the additionality test by the DOEs. These analyses provide evidence that project

developers can obscure the attractiveness of their projects to increase the likelihood of the

projects being admitted to the CDM program (Michaelowa and Purohit 2007).

Lohmann (2009) discusses theoretical and practical problems associated with accounting

models used for CDM projects. For example, the CDM program assumes that carbon saved via

emission reduction is identical to carbon represented by an emissions allowance. This is the

result of politics rather than accounting reality. Calculation of the amount of carbon “saved” by

various classes of projects is difficult because numerous assumptions are required in the

calculation. Lohmann likewise argues that Executive Board members included carbon

consultants and officials from credit-buying countries, who would profit from a high volume of

approved projects. The result is that rule-setting and implementation of CDM regulation is likely

lenient.6

Lohmann (2009) further discusses some of the perverse incentives generated by the way

that CDMs have been implemented. For example, the requirement that projects show they are

economically unviable without the benefit of CERs gives green technology developers an

incentive to keep their products slightly more expensive than less environmentally favorable

alternatives. It also encourages project developers to pay higher prices for the environmental

alternatives.

6 However, as a response to criticism, guidelines and standards have been periodically updated and ethically questionable behavior has become more restricted.

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4. Hypotheses Development

The CDM application and approval process involves a tradeoff between incentive

distortions and external monitoring. When a potential host firm files for CDM program approval,

it has private information about its project’s true future value. The CDM Executive Board, the

DOE, or any other third party cannot costlessly learn this private information. According to the

additionality requirement, the host firm must report an internal rate of return lower than the

benchmark for the project to be approved and thus to obtain CERs. The additionality requirement

thereby provides the host firms with incentives to downwardly bias the reported IRR of their

projects. At the same time, host firms also face potential costs from manipulating the reported

information, which include possible rejection if the manipulation is detected.7,8 Absent explicit

monitoring, host firms have an economic incentive to underreport their IRR.9 Even when the IRR

is already lower than the benchmark rate, underreporting creates a buffer in case some of the

assumptions, practices, and resulting economic outcomes are subject to further scrutiny. The host

firms thus must trade off the benefits and costs of information manipulation when they prepare

and submit the application.

We argue that the host firms’ reported IRRs reflect the trade-off between incentives and

external monitoring. Specifically, we argue that a project that generates higher CO2 reduction is

more likely to understate its project IRR, since its potential gain from the subsidized CERs upon

7 Application for the CDM program includes an application fee, so firms are unlikely to apply if they have a project that they do not think has a chance of being accepted. Application does not guarantee acceptance. In our sample, over 100 projects were rejected. 8 Another potential cost is due to reputational damage if there are specious or exaggerated claims made in the design document. This reputational damage may be to the host firm, the DOE, or the CDM Board/CDM program. In 2005, some Indian projects were rejected because the design documents contained text that had been directly copied from projects in distant districts. The text provided quotations from villagers and labor union leaders about the potential local impact of the projects. 9 It may seem that the host firm only needs to report an IRR lower than the benchmark. However, the benchmark is also chosen by the host firm (albeit sometimes with input from the DNA), and it may be refuted by the CDM Executive Board or DOE. Thus the host firm still has incentive to report as low an IRR as possible.

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the approval of the project is high. The higher the granted subsidy from the CDM program, the

stronger incentive the host firm has to report a lower IRR to guarantee approval of the project.

In addition, while admission to the program is not based upon the magnitude of the CER

benefit, projects that become extremely profitable from the CER revenues may draw additional

scrutiny. For example, a series of projects that destroyed HFC-23, a greenhouse gas byproduct of

refrigerant production, came under public scrutiny because of excessive CER revenue. CERs

expected to be worth $4.7 billion were granted for projects where an alternative technology

would cost $100 million (Schneider 2009). After information about these projects became public,

the CDM Executive Board withdrew its support for HFC-23 projects.10 Firms whose projects

have larger reductions, and therefore stand to profit more from admission to the CDM program,

may thus have an incentive to further bias reported IRRs downward to reduce the appearance of

excessive profit-taking from the CER grants. In sum, we predict a negative association between

the reported IRRs and the CERs.

H1: Reported IRRs are negatively associated with expected quantity of CERs related to

the project.

We also predict that the reported IRRs are generally positively associated with the level

of external monitoring, since more stringent monitoring is likely to reduce the host firms’

incentive and ability to report a falsely low IRR. Two types of external monitoring are especially

important in the CDM process. The first type comes from the DOEs. A good DOE is more likely

to detect misreporting, errors, or both and prevent the downward bias contained in the reported

IRRs filed by the host firms. The quality of the DOEs is thus positively associated with the

10 The CER value far exceeded the cost of production of HFC-23. HFC-23 generation plants were found to be manipulating the system, reducing the amount of HFC-23 produced during periods when the emission reductions were not eligible for CERs and then increasing production once they were eligible (Schneider 2011).

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applying firms’ reported IRRs, since qualified DOEs are less likely to approve the applying

firms’ opportunistic behavior.

The second type of monitoring comes from the macro environment of the host country. If

a host firm resides in an economically and socially more developed country, we argue that there

will be more implicit monitoring by both the national authority and the DOE. Prior studies show

that a country’s GDP and its institutional environment are correlated (Knack and Keefer 1995)

and the institutional environment also helps to execute and enforce legal norms and standards

(Scully 1988). Additionally, economic development (i.e., a high GDP) is likely to reduce the host

firm’s need for the investment cash inflow. The environmental footprint of a country is also

related to monitoring. Countries with higher carbon emissions tend to be more developed. As a

result, they likely face additional scrutiny worldwide for their emissions, resulting in more

monitoring. In sum, we expect the reported IRR of a project to be inversely associated with the

host country’s economic and social development and environmental footprint.

H2: Reported IRRs are positively associated with the level of monitoring.

5. Research Method

4.1. Research design

Additionality requires that the investment be financially unviable without CER revenues

(or have sufficient barriers that make the project otherwise unviable). Although not all projects

provide a benchmark, additionality implies that the IRR for the project should be viewed relative

to a benchmark and that the IRR without granted CERs should be less than the benchmark. In the

case of projects that do not supply a benchmark, the implicit benchmark is the IRR for more

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carbon-intensive alternative projects. We therefore examine the applying firms’ incentive to

downwardly bias their reported IRRs.

Model (1) below explores factors associated with estimated project IRRs.

IRR = β0 + β1BENCHMARK + β2BARRIER + β3REDUCTION + β4GDP + β5CO2R + β6HDIR + β7DOEBAD + β8DOEACCT + ΣβiControli + ε (1)

where:

BENCHMARK = 1 if there is an benchmark reported, 0 otherwise; BARRIER = 1 if the host firm reports a barrier analysis, 0 otherwise; REDUCTION = projected CO2 reduction per year of the crediting period; GDP = gross domestic product in thousands of $US per capita; CO2R = residual from the regression CO2 = β0 + β1GDP + ε, with CO2 = country level CO2

emission per capita; HDIR = residual from the regression HDI = β0 + β1GDP + β2CO2 + ε, where HDI = host

country’s rank of Human Development Index among all countries, with 1 being lowest level of development;

DOEBAD = 1 for every year up to and including the year that the DOE was sanctioned, 0 if the DOE was never sanctioned or for the years following reinstatement of a previously sanctioned DOE;

DOEACCT = 1 if DOE is affiliated with a Big Four auditor, 0 otherwise.

All models include fixed effects for mitigation type, country and year. 11 In effect, this

specification provides an expected IRR based upon individual project characteristics, and the

coefficients on our test variables indicate the association between the test variables and

unexpected IRR. Where possible (depending on the sample characteristics), we also include

fixed effects for registration status, which can be 1) withdrawn by company, 2) rejected by CDM

Executive Board, or 3) registered for the program.

11 Project categorizations are based on CDM Executive Board (2010b), which describes the methodologies authorized by the Executive Board. Example methods include ACM0003, emissions reduction through partial substitution of fossil fuels with alternative fuels or less carbon intensive fuels in cement or quicklime manufacture (a fuel switch method), and AMS-I.C., thermal energy production with or without electricity (renewable energy). We group the 45 methods into eight different types of mitigation, such as greenhouse gas destruction, and renewable energy.

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We expect BENCHMARK to be positively associated with IRR (hypothesis 1), since the

benchmark provides a bright-line criterion for investment additionality. For projects without a

benchmark, a competitive comparison with alternatives likely fosters an incentive to provide

lower IRR estimates for the carbon-efficient alternative so that it can be deemed to be the least

desirable alternative. We also expect BARRIER to be positively associated with the reported IRR,

since the barrier analysis could qualify a project as “additional” even without a reported rate of

return lower than the benchmark rate. An applying firm with a significant investment barrier

therefore does not have incentives to downwardly bias estimated IRR.

We expect REDUCTION to be inversely associated with IRR, since a higher amount of

reduction implies a higher benefit from the granted CERs and therefore higher incentive for the

applying firm to downwardly bias the IRR. The effect of reductions on applying firms is likely

related to incentives rather than to acceptance into the program. 12 The magnitude of the benefit

from CERs is not included in the CDM Executive Board’s decision to accept a project.

Additionality requires that there be some level of carbon reduction and that the project be

financially unviable in the absence of CERs, not how profitable it is with CERs.

For the monitoring variables, we include measures of the host country’s wealth, carbon

emissions, and level of development. Wealthier and more-developed countries tend to have

stronger institutions and more resources for monitoring. Countries with relatively high carbon

emissions also tend to be more developed and likely face more scrutiny worldwide in their

environmental initiatives, resulting in more monitoring. Our measure of the host country’s

wealth is per capita gross domestic product (GDP). Our measure of carbon emissions is based

upon metric tons per capita of carbon emissions from burning fossil fuels and manufacture of

12 We note that less profitable projects are also more likely to apply to the program. However, our controls for country, project methodology, and year reduce the likelihood that the coefficient we estimate is driven by inherent project characteristics rather than bias.

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cement. GDP per capita and CO2 emissions per capita are available from the World Bank

website (World Bank 2014). Our measure of development is based upon the Human

Development Index (HDI), which is a summary measure comprised of indices for health,

education, and standard of living (United Nations Development Program 2014). HDI is coded so

that a higher rank equates with more development.

We transform the HDI and CO2 measures before they enter the empirical model due to

potential multicollinearity. Richer countries are likely to have higher carbon emissions, and the

(untabled) correlation between CO2 and GDP is 0.39. In addition, because HDI includes standard

of education and health, there is probable multicollinearity between it and the other monitoring

variables. Indeed, the (untabled) correlation between HDI and GDP (CO2) is 0.83 (0.47). We

therefore orthogonalize these monitoring measures by constructing two new variables HDIR and

CO2R, which are defined as the residuals from the regressions HDI = β0 + β1GDP + β2CO2 + ε

and CO2 = β0 + β1GDP + ε, respectively. We retain GDP in its original form. This allows us to

explore the unique impact of different factors associated with monitoring. We expect GDP,

CO2R, and HDIR to be positively related to the reported IRR (hypothesis 2), as countries that are

richer and more developed and have a larger carbon footprint have better monitoring.13

We also include project-level monitoring by examining the type of DOEs that are

involved in the validation process. During our sample period, several DOEs were removed from

the list of approved auditors. These DOEs were sanctioned for a variety of reasons, including

failure to survey projects before authorizing them and inability to prove that staff had properly

audited projects (or were qualified to do so) (Murray 2009). We expect that a sanctioned DOE is

a less effective monitor. We set DOEBAD = 1 in years before and including the year of sanction.

13 Our models include country-level main effects, so the results on the country-level variables are related to the underlying construct rather than driven by differences by country alone.

19

If the DOE is reinstated, we assume that improved monitoring is a requirement for reinstatement

and set DOEBAD = 0 for subsequent years. We argue that less effective monitoring will be less

likely to deter firms from downwardly biasing their reported IRRs. DOEBAD should thus be

negatively related to IRR.

Many of the DOEs are engineering consultants, who focus more on the physical aspects

of the projects than on the financial ones. Relative to accountants, they are less knowledgeable

about project finances and are less likely to detect a bias in estimates. Accountants, in particular,

those from firms that provide higher audit quality, should be more likely to detect bias.

Following the audit quality literature, we code the dummy variable DOEACCT as 1 if the DOE is

affiliated with a Big Four accounting firm. Because of superior monitoring, managers should not

be able to respond to incentives to decrease reported IRR and DOEACCT should thus be

positively related to the reported IRR.

In addition to our main hypothesis tests, we explore monitoring and incentive issues for

two additional groups of projects: 1) projects that have a negative IRR and 2) projects that have

IRRs in excess of the stated benchmark.

A number of projects in our sample have associated revenues, but the costs exceed the

revenues. These projects should be reported as having an incalculable IRR, although our sample

contains a number of such projects that report a negative point estimate for IRR. We explore

whether the incidence of these erroneous reports is related to monitoring at the country or project

level (or both). We employ a logistic regression described in model (2) below:

NEGATIVE_IRR = β0 + β1BARRIER + β2REDUCTION + β3GDP + β4CO2R + β5HDIR + β6DOEBAD + ΣβiControli + ε (2)

where:

NEGATIVE_IRR = 1 if a negative expected IRR is reported as incalculable, 0 otherwise.

20

Other variables are defined above.

In general, we expect that a higher level of monitoring is more likely to be associated

with a higher probability of correctly reporting a negative IRR as incalculable (NEGATIVE_IRR

= 1), since monitoring reduces the chance of the applying firms misreporting opportunistically or

through ignorance. We thus expect GDP, CO2R, and HDIR to have likelihood ratios significantly

above one. Due to the inferior monitoring of sanctioned DOEs, we expect DOEBAD to have a

likelihood ratio significantly below one. We include BARRIER because we expect that projects

containing a relatively more sophisticated analysis of barriers are likely to be of higher quality

overall and therefore less likely to have this type of reporting error. We also include

REDUCTION since the cost of having the project rejected due to poor reporting is higher for

high-reduction projects. As in model (1), we include year, country, and mitigation type fixed

effects. All of the projects reporting negative IRRs were accepted into the program, so we do not

include any fixed effects for registration status. None of the projects with Big Four-related DOEs

reports having a negative IRR.

Projects with IRRs in excess of the benchmark do not satisfy the typical additionality

criteria and should not be accepted into the CDM program unless there are significant barriers to

their implementation. This means that there is no incentive to report an IRR above the

benchmark. There are two reasons why registered projects might have an IRR above the

benchmark. First, the IRR was erroneous and the project should have been rejected from the

program but the errors were not detected. Second, the project was not registered in error;

although the IRR is higher than the benchmark, there are significant barriers to its

implementation. The level of monitoring should be related to the first reason. Project

characteristics should be related to the second reason.

21

Our estimation model is:

ABOVE_BM = β0 + β1BARRIER + β2REDUCTION + β3GDP + β4CO2R + β5HDIR + + β6DOEBAD + ΣβiControli + ε (3)

where:

ABOVE_BM = 1 if a reported IRR is higher than the benchmark and 0 otherwise Other variables are defined above.

Our analysis includes all positive IRR projects. Successful project registration for

projects with expected IRRs above the benchmark should require a barrier analysis. Without

significant barriers to investment, a project with an IRR above benchmark cannot be deemed

additional. Barrier analyses are less helpful for success in project registration for projects where

the estimated IRR is below the benchmark, because a barrier analysis is not included in

determination of additionality. We thus expect firms that have conducted a barrier analysis to be

more likely to report an IRR higher than the benchmark.

In model (3), the role of our country-level variables is less clear. As we have argued,

more intensive monitoring likely reduces the incidence of misreporting. If the IRRs above

benchmark represent errors where the estimated IRRs should actually be below benchmark, then

we would expect negative coefficients on HDIR, CO2R, and GDP variables due to superior

monitoring. However, because projects facing sufficient barriers to investment can be accepted

into the CDM program even if the IRR exceeds the benchmark, it is unlikely that all projects

with reported IRR above the benchmark have erroneous IRR estimates. Another possibility is

that higher values for HDIR, CO2R, and GDP indicate increased barriers to investment.14 For

example, a larger carbon footprint may indicate a greater risk of future regulation, which can

14 Inclusion of the BARRIER in the model provides an estimate of the main effect for barriers. The country-level variables provide estimates of slope effects related to different types of barriers.

22

reduce the opportunity set for carbon-efficient investment. Or it may indicate that these types of

projects have faced barriers, resulting in fewer past investments. There may also be institutional

barriers that make carbon reducing projects more difficult to implement, such as political,

legislative, or cultural restrictions.

Our expectations for DOEBAD in model (3) are also unclear. If sanctioned DOEs are

poor monitors and allow projects that erroneously reported an IRR greater than the benchmark,

we expect DOEBAD to have a likelihood ratio significantly greater than one. Similarly, if

managers of applicant firms have justified their registration with an IRR greater than the

benchmark based upon an overstatement of barriers to investment, poor quality DOEs may not

have the skills to determine that. However, if firms want to justify additionality on the basis of a

more sophisticated argument, which entails detailed exploration of barriers to investment, they

may seek out higher-quality DOEs, resulting in a lower probability of hiring a poor quality DOE.

4.2. Sample

We draw our sample of projects from the IGES CDM Project Database

(http://www.iges.or.jp/en/cdm/report_cdm.html#cdm_a). The first CDM project was registered

in late 2004 (Norton Rose 2009), and the projects represented in the entire database span late

2004 through December 2012. Few projects start in 2004, so our sample period begins in 2005.

To ensure the representativeness of our sample, we select eight countries as the hosts of our

sample firms. These are Brazil, China, India, Indonesia, Malaysia, Mexico, Peru, and Thailand.

We selected these countries because they provide a broad representation of countries in Asia and

South America. African countries host few projects, so they were not included. Together, the

sample countries host a large proportion of the CDM projects. For projects listed as of March,

2012, these eight countries represent over 84% (4,886 out of 5,790) of the CDM projects

23

worldwide. Once we delete projects that do not have complete data or, due to their nature, do not

have a reported IRR (e.g., projects with no associated revenues are only required to do a cost

analysis), our final sample comprises 2,510 projects. Out of this sample, 24 projects were

withdrawn from consideration by the host, 105 were rejected from the program, and 2,381 were

registered and can earn CERs. Table 2 presents the sample by year and by host country. China

and India have the largest number of projects (1,476 and 644, respectively). Table 3 presents

descriptive statistics for the variables. The average reported IRR of the sample firms is 7.88%.

Out of 161 firms that reported a negative IRR, 62.73% reported it as undefined. The average

reported benchmark IRR rate is 10.92%, and 49.12% of the firms used a barrier analysis in their

reports. The average CO2 reduction is more than 16,000 tons per year.

Table 4 provides the correlation among all key variables. BARRIER is significantly and

negatively correlated with most of the other variables, except DOEACCT. REDUCTION is

positively correlated with all the country-level variables. The country-level variables are

generally inversely related to DOEBAD, which indicates more developed countries are less likely

to have sanctioned DOEs.

4.3. Results

Table 5 reports results of a robust regression estimation of model (1).15 Consistent with

our expectations, we find that BENCHMARK and BARRIER are both positively associated with

IRR, indicating that the applying firms that reported a benchmark rate, a barrier analyses, or both

are less likely to manipulate their reported IRR. REDUCTION measures the potential benefit the

applying firm could obtain once the project is additional and is significantly inversely associated

with IRR. Among the monitoring variables, country-level GDP and HDIR as well as DOEACCT

15 Collinearity diagnostics indicate that none of the variables have a VIF over 1.4, and the highest condition index is 17.95.

24

are significantly positively related with the reported IRR, indicating that effective monitoring has

a positive effect on the applying firms’ reporting behavior.16

Table 6 reports results of estimating model (2) via robust logistic regression. Entries in

the table are the likelihood ratio estimates. Thus a statistically significant value greater than one

represents a greater likelihood of firms rationally reporting a negative IRR project as having an

incalculable IRR. A statistically significant value lower than one represents a greater likelihood

of firms incorrectly reporting a negative point estimate for IRR. Consistent with increased

monitoring, we find that firms in countries with more relatively higher carbon emissions are

more likely to properly report a negative IRR. Results also indicate that sanctioned DOEs

(DOEBAD) are less likely to correctly report negative IRR projects. This is likely due to the lack

of expertise of sanctioned DOEs for calculating IRRs appropriately, which reflects their inability

to appropriately monitor applying firm financial analyses.17

Table 7 provides the robust logistic regression results for model (3). The analysis

includes only those projects with positive IRRs. Consistent with our expectation, we find having

a barrier analysis significantly increases the likelihood of having a reported IRR that exceeds the

benchmark. We also find that countries with excessive carbon emissions per capita are more

likely to have projects with IRRs greater than the benchmark. This is consistent with additional

institutional complexity and increased barriers for carbon efficient investment for these

countries. Finally, sanctioned DOEs are significantly less likely associated with projects having

16 We also ran equation (1) including fixed effects by method, rather than mitigation type. Results are qualitatively similar. We report estimation of model (1) using mitigation type fixed effects to be consistent with the fixed effects used in models (2) and (3). We do not run the models (2) and (3) with method fixed effects due to sample considerations. For example, for model 2, the incidence of negative IRR projects is relatively low, so several methods have only one project with a negative IRR. This makes model estimation with method-level fixed effects problematic. 17 As a sensitivity analysis, we ran the logistic analysis using conditional logit with fixed effects, grouped by year. Results are qualitatively similar, although HDIR is also positively related to the likelihood of having a negative IRR reported correctly (p = 0.093).

25

IRRs above benchmarks. This may be because applying firms requiring expertise to justify a

barrier-based registration (as opposed to financial additionality) may seek out different types of

DOEs than firms that argue additionality through the financial unviability of projects. Such

DOEs are more likely to have expertise in the engineering and regulatory aspects of projects

rather than financial expertise. We note that no projects with Big Four-related DOEs have IRRs

above the benchmark. This is consistent with use of a barrier-based rather than a financial-based

argument for registration.18

6. Discussion and Conclusions

Climate change presents increasing challenges on issues involving accounting regulators

and practitioners. As the market approach to control climate change has become a dominant

approach in practice by governments and businesses, valuation of instruments and investments

underlying the markets and associated disclosures are critical to the markets’ effective operation.

However, guidance for valuation and disclosure is still developing, and there is scant related

research in the accounting literature. To our knowledge, this study is the first to investigate

reporting bias in valuation of a broad cross-section of CDM projects and the underlying

incentives.

Consistent with our expectation, we find that reported rates of return by host firms tend to

downwardly bias the value of their projects to increase the probability of acceptance into the

CDM program. However, monitoring seems to mitigate the distorted incentives and related

misreporting. Specifically, the host country’s GDP, level of development and CO2 emission per

capita are significantly related to the applying firms’ reporting behavior. Furthermore, the quality

of DOEs also play an important role. A DOE that has been sanctioned seems associated with

18 As a sensitivity analysis, we ran the logistic analysis using conditional logit with fixed effects, grouped by year. Results are qualitatively similar.

26

poorer reporting quality of the applying firms, and DOEs associated with one of the Big Four

accounting firms are the opposite. These results underscore the important role that monitoring

can play in diverse settings to mitigate adverse incentives.

27

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Figure 1

Project Stages for a Successfully Implemented CDM Project*

Project Status Participants Resulting documents Development Project owner/project developer Project design document Approval Host country Designated National Letter of approval Authority (Host and Annex 1 country) Validation Designated Operating Entity (DOE1)** Validation report Registration CDM Executive Board CDM Executive Board decision Monitoring Project owner Monitoring report Verification DOE2 Verification report Certification DOE2 Certification report Issuance of CERs CDM Executive Board, CDM registry * Projects that fail to pass a stage can be revised and resubmitted. ** DOE1 and DOE2 may or may not be the same organization. Adapted from: UNFCCC (2001)

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Table 1

Carbon Mitigation Types

Method Title # projects Greenhouse Gas (GHG) destruction ACM0001 Flaring or use of landfill gas 54ACM0008 Abatement of methane from coal mines 40

ACM0010 GHG emission reductions from manure management systems 3

ACM0014 Treatment of wastewater 5AM0003 Landfill gas capture projects 2

AM0006 GHG emission reductions from manure management systems 1

AM0013 Avoided methane emissions from organic waste-water treatment 3

AM0016

GHG mitigation from improved animal waste management systems in confined animal feeding operations 36

AM0022 Avoided wastewater and on-site energy use emissions in the industrial sector 6

AMS-III.D. Methane recovery in animal manure management systems 4AMS-III.G. Landfill methane recovery 5

AMS-III.H. Wastewater treatment in new anaerobic facility and existing aerobic facility 64

AMS-III.R. Methane recovery in agricultural activities at household/small farm level 1

Renewable energy

ACM0002 Grid-connected electricity generation from renewable sources 1083

ACM0006 Consolidated methodology for electricity and heat generation from biomass 57

ACM0018 Electricity generation from biomass residues in power-only plants 3

AM0005 Small grid-connected zero-emissions renewable electricity generation 1

AM0015 Bagasse-based cogeneration connected to an electricity grid 4

AM0036 Fuel switch from fossil fuels to biomass residues in heat generation equipment 1

AMS-I.A. Electricity generation by the user 6AMS-I.C. Thermal energy production with or without electricity 39AMS-I.D. Grid connected renewable electricity generation 809

AMS-I.F. Renewable electricity generation for captive use and mini-grid 2

34

Fuel switch

ACM0003 Partial substitution of fossil fuels in cement or quicklime manufacture 8

ACM0009 Consolidated baseline and monitoring methodology for fuel switching from coal or petroleum fuel to natural gas 2

ACM0011

Consolidated baseline methodology for fuel switching from coal and/or petroleum fuels to natural gas in existing power plants for electricity generation 1

AM0009 Recovery and utilization of gas from oil fields that would otherwise be flared or vented 2

AMS-III.AS. Switch from fossil fuel to biomass in existing manufacturing facilities for non-energy applications 1

Energy efficiency

ACM0004 ver. 2 Consolidated methodology for waste gas and/or heat for power generation 58

ACM0007 ver. 4 Conversion from single cycle to combined cycle power generation 1

ACM0012 ver. 3 Consolidated baseline methodology for GHG emission reductions from waste energy recovery projects 61

ACM0013 ver. 4

Construction and operation of new grid connected fossil fuel fired power plants using a less GHG intensive technology 3

ACM0016 ver. 3 Mass rapid transit projects 1AM0014 ver. 3 Natural gas-based package cogeneration 2AM0018 ver. 1 Steam optimization projects 1

AM0024 ver. 2 Greenhouse gas reductions through waste heat recovery and utilization for power generation at cement plants 13

AM0032 Methodology for waste gas or waste heat-based cogeneration system

1

AM0055 Recovery and utilization of waste gas in refinery or gas plant 2

AM0059 Reduction in GHGs emission from primary aluminum smelters 1

AM0062 ver. 2 Energy efficiency improvements of a power plant through retrofitting turbines 1

AMS-II.C. ver. 11 Energy efficiency improvements of a power plant through retrofitting turbines 3

AMS-II.D. ver. 12 Energy efficiency and fuel switching measures for industrial facilities 9

AMS-II.H. ver. 3 Energy efficiency measures through centralization of utility provisions of an industrial facility 2

AMS-III.AL. Conversion from single cycle to combined cycle power generation 1

AMS-III.P. Recovery and utilization of waste gas in refinery facilities 1AMS-III.Q. ver. 4 Waste energy recovery (gas/heat/pressure) projects 9

35

Feedstock switch

AM0033 Use of noncarbonated calcium sources in the raw mix for cement processing 2

GHG emission avoidance AM0025 Alternative waste treatment processes 13

AM0039 ver. 2 Methane emissions reduction from organic waste water and bioorganic solid waste using co-composting 7

AM0065 Replacement of SF6 with alternate cover gas in the magnesium industry 1

AMS-III.E. ver. 16

Avoidance of methane production from decay of biomass through controlled combustion, gasification, or mechanical/thermal treatment 3

AMS-III.F. ver. 10 Avoidance of methane emissions through composting 35

AMS-III.Y. Methane avoidance through separation of solids from wastewater or manure treatment systems 1

Low carbon electricity

AM0029 Grid-connected electricity generation plants using natural gas 30

Afforestation and Reforestation AR-ACM0001 Afforestation and reforestation of degraded land 2AR-AM0001 Reforestation of degraded land 1

AR-AM0003

Afforestation and reforestation of degraded land through tree planting, assisted natural regeneration, and control of animal grazing 2

Codes were developed based upon categories from the United Nations CDM Methodology Booklet (CDM Executive Board 2010b).

36

Table 2

CDM Projects by Host Country and Year

Year Brazil China India Indonesia Malaysia Mexico Peru Thailand Total

2005 1 2 6 3 1 0 0 0 13

2006 38 26 38 3 1 17 0 0 123

2007 7 99 75 3 10 6 2 3 205

2008 10 203 59 5 9 2 4 3 295

2009 12 365 57 6 28 6 5 9 488

2010 3 503 100 14 8 3 2 10 643

2011 11 222 149 12 11 7 3 19 434

2012 21 56 160 19 6 17 15 15 309

Total 103 1476 644 65 74 58 31 59 2510

37

Table 3

Summary Statistics

Variable

Obs.

Mean

Std. Dev.

Min.

Max.

IRR 2346 0.0788 0.0345 0.0013 0.7363RATIONAL_ IRR 161 0.6273 0.4850 0 1 BENCHMARK 2434 0.1092 0.0313 0 0.2959BARRIER 2510 0.4912 0.5000 0 1 REDUCTION (000 tons) 2510 16.1950 29.5752 0.1095 395.9752HDI 2510 76.3647 18.2963 48.0000 127.5 GDP 2510 3.8627 2.1673 0.7400 12.576 CO2 2510 4.4578 2.0486 1.300 7.8

IRR is the applying firm’s reported rate of return. RATIONAL_IRR is 1 if a negative IRR is reported as uncalculable, 0 otherwise. BENCHMARK is 1 if there is a benchmark reported, 0 otherwise. BARRIER is 1 if the host firm reports a barrier analysis, 0 otherwise. REDUCTION = CO2 reduction per year of the crediting period. HDI is the rank of Human Development Index among sample countries, with 1 being least developed. GDP is the gross domestic product in thousands of $US per capita. CO2 is the CO2 emission per capita.

38

Table 4

Correlation Matrix

BARRIER

REDUCTION

HDIR

CO2R

GDP

DOEBAD DOEACCT

REDUCTION -0.1999 1.0000 0.0000 HDIR -0.1445 0.0860 1.0000 0.0000 0.0000 CO2R -0.3880 0.1549 0.3373 1.0000 0.0000 0.0000 0.0000 GDP -0.2222 0.0765 0.2948 0.2629 1.0000 0.0000 0.0001 0.0000 0.0000 DOEBAD 0.0676 -0.0010 -0.0229 -0.1650 -0.1266 1.0000 0.0007 0.9594 0.2520 0.0000 0.0000 DOEACCT -0.0407 0.0236 0.0235 0.0812 0.0352 -0.0425 1.0000 0.0417 0.2378 0.2398 0.0000 0.0780 0.0334 REDUCTION is the CO2 reduction per year of the crediting period. HDI is rank of Human Development Index among sample countries, with 1 being least developed. CO2 is the CO2 emission per capita. GDP is the gross domestic product in thousands of $US per capita. HDIR is the residual from the regression HDI = β0 + β1GDP + β2CO2 + ε. CO2R is the residual from the regression CO2 = β0 + β1GDP + ε. DOEBAD is 1 for the year if DOE was sanctioned, 0 if the DOE was never sanctioned or the year a sanctioned DOE was reinstated. DOEACCT is 1 if DOE is affiliated with a Big Four audit firm, 0 otherwise.

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Table 5

Robust regression of factors affecting reported IRR, for positive IRR projects

Predicted

Sign

IRR

Coefficient

P-value BENCHMARK + 0.0118 0.000 BARRIER + 0.0063 0.000 REDUCTION - -0.0000 0.003 GDP + 0.0102 0.000 CO2R + 0.0003 0.160 HDIR + 0.0015 0.000 DOEBAD - 0.0000 0.979 DOEACCT + 0.0068 0.029 Intercept 0.0250 0.043 Year fixed effects Yes Mitigation type fixed effects

Yes

Country fixed effects Yes Registration Status fixed effects

Yes

N 2,346 F(68, 2273) 92.65 0.0000

The sample consists of 2,342 projects. The regression model is IRR = β0 + β1BENCHMARK + β2BARRIER + β3REDUCTION + β4GDP + β5CO2R + β6HDIR + β7DOEBAD + β8DOEACCT + ε. Two-tailed p values are reported in the last column. BENCHMARK is 1 if there is a benchmark reported, 0 otherwise. BARRIER is 1 if the host firm reports a barrier analysis, 0 otherwise. REDUCTION is the CO2 reduction per year of the crediting period. HDI is the rank of Human Development Index among sample countries, with 1 being least developed. CO2 is the CO2 emission per capita. GDP is the gross domestic product in thousands of $US per capita. HDIR is the residual from the regression HDI = β0 + β1GDP + β2CO2 + ε. CO2R is the residual from the regression CO2 = β0 + β1GDP + ε. DOEBAD is 1 for the years up to and including year of sanction if DOE was sanctioned, 0 if the DOE was never sanctioned or the year a sanctioned DOE was reinstated. DOEACCT is 1 if DOE is affiliated with a Big Four audit firm, 0 otherwise.

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Table 6

Logistic regression of factors related to differential reporting for negative IRR projects

Predicted Value

NEGATIVE _IRR

Coefficient

P-value BARRIER >1 0.7191 0.530 REDUCTION >1 0.9998 0.987 GDP >1 6.2565 0.144 CO2R >1 12.2292 0.003 HDIR >1 1.3541 0.080 DOEBAD <1 0.0680 0.001 Intercept 0.0003 0.578 Year fixed effects Yes Mitigation type fixed effects

Yes

Country fixed effects Yes N

155

Log Pseudolikelihood -72.24 0.000 LR Chi2(22) 63.63 Pseudo R2 0.30

The sample consists of 155 projects that reported an IRR as negative. The regression model is NEGATIVE_IRR = β0 + β1BENCHMARK + β2BARRIER + β3REDUCTION + β4GDP + β5CO2R +β6HDIR + β7DOEBAD + ε. The second column presents the odds ratios and the third column presents z-score based p-values. NEGATIVE_IRR is 1 if the reported IRR for a negative IRR project is reported as incalculable, 0 if a negative point estimated is provided. BARRIER is 1 if the host firm reports a barrier analysis, 0 otherwise. REDUCTION is the CO2 reduction per year of the crediting period. HDI Human Development Index among sample countries, with rank1 being highest, on GDP and CO2. CO2 is carbon dioxide emission per capita. GDP is the gross domestic product in thousands of $US per capita. HDIR is the residual from the regression HDI = β0 + β1GDP + β2CO2 + ε. CO2R is the residual from the regression CO2 = β0 + β1GDP + ε. DOEBAD is 1 for the years up to and including year of sanction if DOE was sanctioned, 0 if the DOE was never sanctioned or the years following reinstatement of a sanctioned DOE.

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Table 7

Logistic regression of factors associated with reported IRR above benchmark, for positive IRR projects

Predicted Value

ABOVE_BM

Coefficient

P-value BARRIER >1 2.3353 0.001 REDUCTION <1 0.9909 0.117 GDP ? 1.7302 0.188 CO2R ? 2.0921 0.060 HDIR ? 1.046 0.451 DOEBAD ? 0.5076 0.053 Intercept 0.6625 0.892 Year fixed effects Yes Mitigation Type fixed effects

Yes

Country fixed effects Yes N 2348 Log Likelihood -329.98 LR Chi2(25) 524.09 0.000 Pseudo R2 .44

The regression model is ABOVE_BM = β0 + β1BENCHMARK + β2BARRIER + β3REDUCTION + β4GDP + β5CO2R +β6HDIR + β7DOEBAD + ε. Two-tailed p values are reported in parentheses. ***/**/* represent statistical significance at 1%/5%/10% levels. BARRIER is 1 if the host firm reports a barrier analysis, 0 otherwise. REDUCTION is the CO2 reduction per year of the crediting period. HDI is the rank of Human Development Index among sample countries, with 1 being highest. CO2 is the CO2 emission per capita. GDP is the gross domestic product in thousands of $US per capita. HDIR is the residual from the regression HDI = β0 + β1GDP + β2CO2 + ε. CO2R is the residual from the regression CO2 = β0 + β1GDP + ε. DOEBAD is 1 for the years up to and including year of sanction if DOE was sanctioned, 0 if the DOE was never sanctioned or the year a sanctioned DOE was reinstated.