The financial attractiveness assessment of large waste management projects registered as clean development mechanism

Waste Management xxx (2015) xxx–xxx

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Waste Management journal homepage: www.elsevier.com/locate/wasman

The financial attractiveness assessment of large waste management projects registered as clean development mechanism André Luiz Bufoni a,⇑, Luciano Basto Oliveira b, Luiz Pinguelli Rosa a a b

Energy Planning Program, Universidade Federal do Rio de Janeiro PPE/COPPE/UFRJ, Brazil International Virtual Institute of Global Changes IVIG/COPPE/UFRJ, Brazil

a r t i c l e

i n f o

Article history: Received 10 February 2015 Revised 17 June 2015 Accepted 21 June 2015 Available online xxxx Keywords: Finance Attractiveness Return Benchmark Kyoto CDM

a b s t r a c t This study illustrates the financial analyses for demonstration and assessment of additionality presented in the project design (PDD) and enclosed documents of the 431 large Clean Development Mechanisms (CDM) classified as the ‘waste handling and disposal sector’ (13) over the past ten years (2004–2014). The expected certified emissions reductions (CER) of these projects total 63.54 million metric tons of CO2eq, where eight countries account for 311 projects and 43.36 million metric tons. All of the projects declare themselves ‘not financially attractive’ without CER with an estimated sum of negative results of approximately a half billion US$. The results indicate that WM benchmarks and indicators are converging and reducing in variance, and the sensitivity analysis reveals that revenues have a greater effect on the financial results. This work concludes that an extensive financial database with simple standards for disclosure would greatly diminish statement problems and make information more comparable, reducing the risk and capital costs of WM projects. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction During the first Kyoto Protocol commitment period (CP1, 2008–2012), almost one thousand (949) waste management projects were implemented in developing countries (DCs), which utilized incentives from the Protocol (Article 12). The CERs, a main product of carbon initiatives, ‘‘can be traded and sold, and used by industrialized countries to meet a part of their emission reduction targets’’ (UNFCCC, 2014). The credits could be used for Europe Union emissions (AAUs) of some 1.6 billion tonnes of CO2 between 2008 and 2020. This represents half of the emissions reduction in the EU Emissions Trade System during that period (European Comission, 2013). In many cases, CERs were not the focus of these projects, but represented the only revenue. Thus, despite the potential advantages of carbon reductions activities, technical and financial support remain the major barriers in most countries (Barton et al., 2008; UNFCCC, 2008). If not for the financial resources provided by the credits, the projects ‘‘would never be implemented’’. Expectations for Kyoto’s second commitment period (CP2, 2013–2020) are far different. In 2012, the CER prices collapsed, ⇑ Corresponding author at: Faculdade de Administração e Ciências Contábeis, Av. Pasteur, 250, Sala 247 – Campus da Praia Vermelha, Rio de Janeiro, RJ 22.290-240, Brazil. E-mail address: [email protected] (A.L. Bufoni).

and by the end of 2013 the number of proposed transactions reached its lowest value since 2008 (ITL, 2013). Market participants consider the major factors to have been the 2008 economic crisis, the fact that the CP1 emissions balance in developed countries is overestimated by 12Gt, even if no AAUs from CP1 were carried over to CP2, and that important countries, such as Russia, Canada and Australia, did not join the second period (Point Carbon, 2012). Therefore, the future registration of waste management projects as CDMs is uncertain, improvements to existing technology projects will be postponed, and financial analyses without CER revenues increase in importance. Recent studies indicate that waste management research pays little attention to financial issues, and does not operate in a broad, systemic view (Allesch and Brunner, 2014; Singh et al., 2014). Financial articles are mainly associated with scenarios, complex LCA (Martinez-Sanchez et al., 2015) and arbitrary CBA, usually within a project’s boundaries and methodologies (see Karmperis et al., 2013). Industry databases and inventories, which can significantly aid policy makers in DCs, are also very scarce (Inanc et al., 2004). Thus, the UNFCCC database on waste management financial analysis indicators is a comprehensive and valuable tool for projects, industries and countries. However, the information in the database is not tabulated, and simply joined to the projects’ individuals.

http://dx.doi.org/10.1016/j.wasman.2015.06.030 0956-053X/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Bufoni, A.L., et al. The financial attractiveness assessment of large waste management projects registered as clean development mechanism. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.06.030

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx

Abbreviation AAU AD AE AWMS BAU CAPEX CAPM CBA CDM CER CP DC EFB ETS HIRN IMF IRR

Assigned Amount CO2eq Unit Anaerobic Digestion Applicant Entities Animal Waste Management System Business as Usual Capital Expenditures Capital Asset Price Model Cost-Benefit Analyses Clean Development Mechanism Certified Emission Reductions Kyoto’s Commitment Period Developing Countries Palm Empty Fruit Bound Emission Trade System Rankine with Superheating International Monetary Fund Internal Rate of Return

Therefore, the aim of this work is to compare the financial analysis information presented by the project design documents of the 431 large Clean Development Mechanisms (CDM) used, classified as ‘‘waste handling and disposal sector’’ (13) over the past ten years (2004–2014). The objective of this study is to analyze the following aspects of the projects: (1) a sample detailed description; (2) disclosure extension and quality; (3) attractiveness indicators; (4) revenues; (5) capital and operational expenses; and (6) the sensitivity analysis to discuss the results and derive conclusions. 2. Material and methods This research applies the manual hypertexts content analysis strategy to describe and make inferences about the precursors, characteristics and the expected effects of the PDD, their appendices and other enclosure documents submitted to obtain the CDM registration under the UNFCCC. The section of the PDDs being analyzed is the ‘‘Application of a baseline and monitoring methodology’’. ‘‘Tool for the demonstration and assessment of additionality’’ and ‘‘Investment Analysis’’ relies on that section. With the continuous improvement of disclosure, it became common to submit the financial information in separate sheets from the original document, which was also verified to complete the missing information. In total, we verified 431 large waste management projects extracted from the Project Search on the CDM site (UNFCCC, 2014), using the following criteria: ‘‘Waste handling and disposal (13)’’ for sectorial scope, ‘‘Large’’ for scale and ‘‘Registered’’ for status. The data were sorted by the four digit reference number. The 163 numbers cited were used to link the evidence to the statements made in this work. For example, (0008) refers to the Brazil NovaGerar Landfill Gas to Energy Project, which is summarized in Table B.1. The content was verified individually, where each project’s classification and financial information were transferred manually, one by one, to a worksheet. Then, for all relevant cases, the spreadsheet was used to analyze ranges in subsamples, as well as widespread trends. Because the data are mainly monetary, we used the traditional robust standard errors regression and variance analysis and Spearman correlation methods to verify significance of mean differences and relations within indicators, industry types and countries (Hamilton, 2009). The cognitive capacity of the analysts is a significant limitation, as is the quality of the content and related analysis. To reduce

LCA LFG MSW NPV OPEX OFMSW PAYT PDD PoA POME RBMCA RDF UNFCCC WACC WTE

Life Cycle Assessment Landfill Gas Municipal Solid Waste Net Present Value Operational Expenditures Organic Fraction of Municipal Solid Waste Pay As You Throw Project Design Document Program of Activities Palm Oil Mill Effluent Risk-Based Multi-Criteria Assessment Refuse Derived Fuel United Nations Framework Convention on Climate Change Weighted Average Capital Cost Waste to Energy

potential error, all information was double checked. Another, less obvious, limitation is that financial conditions are related to barriers declared in the projects, which often establish causal relationships. These barriers will be addressed in other studies. Since all considerations are solely based on information available and collected before the projects have been implemented, the actual project performance is not considered for this study. However, the actual project performance may have a significant influence on the financial attractiveness of the individual CDM during its lifetime, and should therefore be considered a method limitation. Nevertheless, the project’s attractiveness greatly determines the decision to implement it, and this is the main utility of this work. 2.1. Financial requirements and methodologies Several financial requirements must be met to allow CDM AEs to conduct project activity, or program of activities (PoA), validation, and receive CDM accreditation. Some requirements are standards to any sector or scale, such as financial stability and risk exhibition (UNFCCC, 2009), while others refer to sectoral design requirements, where the participant may select a CDM baseline and monitoring methodology (UNFCCC, 2011). In this articles waste sector context, the classifications used are: 252 landfill (ACM0001), 60 manure (ACM0010), 51 wastewater (ACM0014), 39 incinerator, 23 composting, 1 gasification, 2 sewage sludge processes and 3 refused derived fuel (ACM0022). Because establishing baseline scenarios and additionality identification are often difficult and non-uniform, the UNFCCC has also developed a methodological tool (TOOL2, 2006) to help with the analysis. An earlier version was used only in wastewater projects (TOOL1, 2004). Both recommend that projects should take into account the ‘‘Guidelines on the assessment of investment analysis’’ (UNFCCC, 2010, p. Annex 5), which are available on the UNFCCC website when completing the Investment Analysis step (2 or 3). TOOL1 divides the analysis into the following steps: (a) determine appropriate analysis method; (b) apply the analysis method; (c) calculation and comparison of financial indicators; and (d) sensitivity analysis. If the CDM project activity generates no financial or economic benefits beyond CDM related income, then designers apply the simple cost analysis (Option I). Otherwise, they use the investment comparison analysis (Option II) or the benchmark analysis (Option III). Steps (c) and (d) are only available when a project uses Option II or III.

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx

2.2. Sample description In South America, Africa and the Middle East, landfill gas projects predominate (90%). In Ecuador and Mexico, the development of agricultural manure technology projects accounts for half of the projects. In Southeast Asia, industrial wastewater composting and anaerobic digestion processes predominate, accounting for 78% of projects in Thailand, and 50% in Viet Nam, Indonesia and Malaysia. China and India are the countries that present the greatest diversity of project types. However, China has experienced an increase in waste projects from landfill gas (48%) and incineration (39%), whereas India utilized composting and refused derived fuel technologies to reduce/divert (55%) from landfilling. The cost-benefit analyses and waste composition make composting a more attractive practice across sub-Saharan Africa (Couth and Trois, 2012). The biggest WTE project is the Shenzhen Baoan Laohukeng Stage II Municipal Solid Waste Incineration Project (60 MW; 6945). Also worthy of note is project (8369), the Manufacture and utilization of a bio-coal Briquette/Pellet Manufacturing Unit in Stutterheim, South Africa. For a reference of complexity in landfill gas utilization, see Doña Juana landfill gas-to-energy project, Colombia (2554). There are three cases of refuse derived fuel (RDF) projects in India, which produce electricity via steam turbines or sell fuel directly (2378, 7790 and 9272). There is only one registered gasification project in Sri Lanka (9104). The MSW projects focus on controlled dump sites and landfills. Landfill gas is the most common project (72%). The projects are divided into phases, where the gas is: (0) vented (common baseline); (1) flared; or (2) used to generate electricity, heat or sent to grid. Phase 1 is always considered, but may utilize a leachate treatment via two-stage submerged combustion (SCE) or conventional evaporation processes (0096, 0851, 1925 and 6804) (Dongbei et al., 2007). Phase 2 information is treated separately, and may or may not be implemented based on LFG production uncertainties (El-Fadel et al., 2012; Han et al., 2009), electricity prices and others industry barriers (3677, 5402, 6363, 8360). In very rare cases, on site separation and sale of recyclables is considered (6486). According to the literature, composting and AD are the two best MSW options for achieving effective carbon reductions in developing countries (Barton et al., 2008; Rogger et al., 2011), but only three cases utilize AD for recovery of biogas from solid waste organic fraction (0938, 2487 and 6254), possibly due to economic, technical and regulatory barriers (Tayyeba et al., 2011). Five others utilize integrated composting (2778, 2867 and 5556), composting with incineration (6680) or the use of engine exhaust for use in steam turbines (9413). Only two projects use steam turbines to produce electricity (1123, 9413). The others projects use reciprocating engines. One landfill avoids landfill gas emissions via in situ aeration (3313) (Ritzkowski and Stegmann, 2010). Animal Waste Management Systems (AWMS) projects usually involve many farms, such as Eco Energy Beer Tuvya (5999) in Israel, with more than 150 facilities, and multiple types of anaerobic digesters. According to the 2006 IPCCC Guidelines for National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and Other Land Use (Chapter 10, Table 10.18), there are 14 different possible scenarios for project activity, and these scenarios are always evaluated as baseline and possible project activities (8508). The typical manure project baseline scenario (BAU) is the uncovered anaerobic lagoon. In many cases, ‘‘this alternative is in compliance with the relevant local and national policies’’ (7018), whereas the project’s proposed activities include biogas production via an anaerobic digestion reactor for local electricity use by gas engines, as well as aerobic treatment of the residues (e.g., activated sludge). This alternative is indicated as the most

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advantageous because it also allows heat production for operation of the digestion process (Prapaspongsa et al., 2010). Wastewater projects, where open anaerobic lagoons are also the most common baseline, activities are typically associated with co-generation thru boilers in Thailand (1040, 2148, 2970, 4155, 6241 and 8874), Malaysia (2427, 3686 and4611) and Indonesia (1176, 4265, 4379 and 4678) and to Jatropha, Molasses, Vinasse, Sugar Beet, Tapioca, Cassava and Palm Oil production (POME and EFB)). In some cases, the project considers combined activities, where a second use of biogas, to generate electricity thru engines (1737, 2144, 6565, 7262, 8874 and 9245) or steam turbines (8593), would be developed. The aerobic treatment cases of EFB and POME are rare (8146) (España-Gamboa et al., 2011; Khalid et al., 2011). We make no differentiation between wastewater, sewage sludge and biomass when they utilize anaerobic treatments, except (3042), which incinerates sewage sludge, and (8369), which manufactures and utilizes bio-coal briquettes. The most recurrent anaerobic digestion technologies used were mesophilic Upflow Anaerobic Sludge Blanket (UASB) Reactors (e.g., 4155, 4265, 4291 and 5364), which is possibly due to the ‘‘ability to maintain a sufficient amount of active biomass’’ (Sakar et al., 2009; Khalid et al., 2011). Other technologies mentioned as suitable choices included Covered Lagoon Bio-Reactors (CLBR), Internal Circulation Reactors (IC Reactor) (3759), Closed Continuous-flow Stirred Tank Reactors (CSTR) (2181, 3686 and 9115), Sequential Batch Reactors (SBR) (2185), Anaerobic Baffled Reactors (ABR) (2110), Modified Anaerobic Baffled Reactors (MABR) (8593), High Concentration Sludge Reactors (HCSR) (5518), Glass-Fused-To-Steel tanks (5105), Continuous Mixed Tank Reactors (CMTR) (6565), Expanded Granular Sludge Beds (EGSB) (4291, 6241) and Multi-Internal Circulation Reactors (MIC) (6988). It is also common for projects to utilize more than one technology (9045). It is not the intention of this work to extend to an analysis of technical aspects, generation or the adequacy of each technology, which can be found in specific literature and in the project design documents. The most common choices for electricity generation from biogas are the 500 kW to 1500 kW (e.g., 8205) reciprocating engines, including brands such as Stamford, MAN, MTU, Guascor, Marelli, Shangdong Shengdong, Luoyang Zhongzhong, Caterpillar and GE Jenbacher. These engines were designed specifically for biogas applications (50% CH4) and are characterized by a particularly high efficiency, low emissions, high durability and high reliability (5518). However, the engines are expensive and require some gas standards to work properly, which may be prohibitive for some projects. Therefore, many projects postpone the inclusion of engines (phase 2) or rule them out altogether (e.g., 0008, 0912, 2518, 4175, 4682 and 5619). Additionally, some projects do not include information on brand or quantity of the reciprocating engines (1505, 4316 and 5861). Multiple smaller engines (3 to 32) are preferred due to increased flexibility in meeting a project’s gas production curve requirements (0052, 0164, 0373, 0822, 2785, 3483, 3370, 4211 and 8508). The first record of an incineration project came from China in September 2010 (3525). China is the location of all 39 incinerators projects, except the Baku Waste to Energy Project in Azerbaijan (7658), and the Barueri Energy CDM Project in Brazil (8128). Both ‘foreign’ projects have many technological uncertainties, and include no financial information. The boilers range from 12 MW to 60 MW, and use 1–4 steam turbines. According to the literature, these turbines are of medium size, at less than 90 MWLHV, but further information on rankine cycle (e.g., HIRN), steam and combined heat and power conditions were not tabulated for comparison with modern incinerators (48.5 MWLHV combustion power, 40 bar/380 °C, no heat recovery and a net electric efficiency of 20.6%LHV) (Lombardi et al., 2015). The incinerator

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manufacturer is typically of Chinese origin, yet these projects declare that they ‘‘do not involve in technology transfer abroad’’ (6073). In some cases, the equipment is imported from Japan (Von Roll L) (8938). The literature indicates, ‘‘that with the rapid progress of the MSW incineration technology [in China], less degradable organic waste might be disposed in landfills, and more MSW incineration residues might be dumped in landfills’’, thereby reducing LFG production (Chen et al., 2010).

2.3. Financial disclosure It is easily verified that a projects’ financial disclosure typically improves with time, in part due to revisions of the UNFCCC methodologies, and also due to the addition of other sources of income beyond the sale of certificates, which makes simple cost analysis (Option I) (51 projects) inappropriate, due to the project’s complexity or benchmark growths. While 153 projects include net present value (NPV) calculations, with only one positive occurrence (4598), 213 projects present the internal rate of return (IRR), of which 162 are positive. No IRRs accomplish the benchmark. 42 additional projects include both indicators. However, 40 projects, mainly classified as landfill gas flare, compost and wastewater, present no financial information (opacity). The absence of information in projects that adopted the Avoided Wastewater and On-site Energy Methodology (AM0022) affirm that, ‘‘Since only one most plausible baseline option can be identified, this investment analysis is not applicable’’ (2076, 2110, 2138, 2144, 2180 and 7018). In some others, the message is simply a brief, ‘‘This step is skipped’’ (e.g., 2374 and 5194), or ‘‘There are not sufficient data available for a regular investment analysis’’ (5460). In other cases, the project declared the information to be ‘‘confidential’’, unavailable (as indicated by a dash) (8126), blank (8242) or ‘‘not chosen and therefore not discussed’’ (7658, 8181). In at least 20 cases, the NPV and IRR indicators are declared without the values or assumptions they use to support their calculations (e.g., 5842). Again, older compost and wastewater initiatives were more likely to have this problem (0169, 1176 and 2427). The format and extension of the financial statements also significantly varies, except when the projects come from the same consultant (e.g., 39 AgCert AWMS), country and industry (China incinerators), which is fairly common. In single consultant manure projects cases, the costs are standardized (e.g., 1000 heads), but specifics are not declared and NPV is the attractiveness indicator used. In part, this disclosure policy favors the uniformity of financial information, but greatly distorts the manure sample statistics. Overall, it is clear that there is no published model or template for the submission of reports, which in many cases is confusing, incomplete and biased. Language errors can be significant, including spelling errors, such as ‘depresiation’, ‘replacment’ and ‘invetsment’, which are repeated in various parts of the reports (e.g., 7777). Currency variety is another difficulty when comparing the data because there are 25 different currencies in the reports. In some reports, the exchange rate between U.S. Dollars or Euros is stated, but in many cases, currencies from Morocco, Indian, Iran, Malaysia, China (RMB, CNY, YUAN), Israel, Vietnam, South Africa, Saudi Arabia, Philippines and Brazil are used indiscriminately, and cannot be easily or accurately translated (5364, 5692). In addition, the listed exchange rates are not always accurate (e.g., 3 BRL = 1 USD or 10 CNY = 1 EUR). Also of note is the recurrent use of ‘‘104 notation’’, rather than the typical listings of thousands or millions, which can add confusion to the projects. In the case of Morocco (5434), the units are operational (dose–response style), and not simply the national currency.

2.4. Attractiveness and rates of return According to the Tool for demonstration and assessment of additionality, discount rates and benchmarks shall be derived from: (1) the CAPM with the adequate risk premium above ‘‘risk free’’ bond rates; (2) lending rates that reflect the comparable project activity cost of financing; (3) WACC of the company under similar conditions; (4) an official government or approved benchmark; and (5) if any of the above are not applicable, other indicators are used. A good explanation can be found in Buenos Aires Complejo Ambiental Norte III (5861) project. Some projects use government bond rates as benchmarks (2751, 3042, 3464, 3958, 6039, 6699 and 7637), which is clearly inadequate. Fortunately, the results remained unchanged because none of them are attractive, even with lower benchmarks. The most used technique is the CAPM, which is often comparatively supported by average energy or waste market risks, and by bank lending rates (8851) when the project receives external finance. Full analysis, based on WACC, is rarely used due to complexity, the need of an earlier equity/debt proportion and adequate yield (2970, 3643). Reference to government official benchmarks are constantly used in such countries as Israel (3820, 6252, 7777 and 8980), where the Public Utility Authority established 15% benchmark, and in the majority of projects in China (3716, 3837, 5326 and 5828), where the State Power Corporation 8% benchmark is used (China Electric Power Press, 2003). Other references include credit rating agencies, such as Fitch Ratings, Moody’s, Standard & Poor’s (e.g., 8603) and the International Monetary Fund (IMF) and World Bank expected return, shown in the ‘‘Guidelines on the assessment of investment analysis’’ (CDM-EB65-A05-STAN, p. Annex 5). The average benchmark declared by each country is depicted in Fig. 1. Using a regression analysis with robust standard errors (weighted), we tested the relationship significance of the project return (NPV and IRR) using the benchmark indicator (independent). The results reject null hypotheses in both cases (t = 4.23; t = 5.13), which establishes correlation with country risk and interest rate. We applied the same test in relative difference of IRR to Benchmark [(IRR b)/b]. The positive and significant (t = 2.25) coefficient (0.01069), indicates higher benchmarks, are also more difficult to reach. Through 2013, benchmark time series countries converged (11–13%), and reduced the overall benchmark variance

Benchmark Ecuador Singapore Pakistan Republic of Korea 25% Maurius El Salvador Dominican Republic Armenia Lao People's… 20% China Brazil 15% Viet Nam Costa Rica 10%

Sri Lanka

Republic of Moldova

5%

Mexico

United Arab Emirates

0%

Jordan

Chile

Saudi Arabia

Israel

Philippines Bolivia Nicaragua Malaysia Morocco Bangladesh Panama Thailand

Argenna Cuba Colombia South Africa India Indonesia Egypt Guatemala

Source: Authors Fig. 1. Benchmark average of host countries.

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx 30% Brazil

25%

Chile China

Benchmark

20%

Colombia India

15%

Indonesia

10%

Israel Malaysia

5%

Mexico

0%

Thailand

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Year

Source: Authors

Viet Nam

Fig. 2. Yearly average benchmark of +10 projects countries.

(chi2 = 31.93), which correlates with a countries stability, interdependence, competition and waste management industry maturation (Fig. 2). The 2014 results represent the first Post-Kyoto benchmark since 2013 actually refers to 2012, which could explain its different behavior. However, due to small sample size (4), the numbers are not significant. Countries that use government official benchmarks tend to be more stable, but they are sample outliers (e.g., China and Israel). Because the attractiveness of the waste management projects in developing countries was the aim of this work, we conducted several tests trying to clarify relationships and significance with others sample variables. The first test verifies if the returns of the industries are different (Ht). The multiple-sample rank sum test (Riffenburgh, 2005, pp. 287–291) rejects the hypothesis that industry returns are from the same population (p = 0.04%). The same trend is observed for host parties (p = 3.27%). These results led us to calculate an IRR means matrix for countries/industries with more than 10 projects (Table 1). Manure (AWMS) projects generally use net present value as an indicator. Net present value is calculated over a range (500–3000), using a 10% interest rate (no matter the benchmark) and for 10

periods. We choose 1000 instances in México and 500 in Brazil, with the electricity from a digester option (project activity). More than 82% (42/51) of projects are in Brazil (20) and Mexico (22), and the results are similar, with values of US$ 127.74–137.96, per instance. In Table 1, standard deviation was represented immediately below the mean value. Dispersion measures are typically used in finance as risk indicators. Table 1 shows that China incineration, and India and Thailand Landfill and RDF projects, have less than 1% standard deviations, while Israel landfill projects seems to be quite different from the others, presenting a higher return and a relative lower standard deviation (mean = 4.84 std. dev.). Then, we test the reliability of return using its relationship with financial and environmental indicators. To reduce linearity and the effect of small IRR values, Gujarati (2004, p. 42) uses a log model [e.g., f(x) = b  ln(CAPEX)]. The correlation table (Table 2) shows that NPV is significantly related to financial indicators, whereas IRR is significantly related to energy indicators (reductions and installed kWe). Correlations suggest that the net present value has a better financial predictive utility or quality than IRR, the meaning of which is unclear. Please note the correlation between installed energy and reductions. Oddly, projects do not always use the benchmark as the interest rate (i), to calculate the NPV. Sometimes they use a higher rate (941, 1301 and 4291), and often a lower rate (1240, 1242, 1307, 1355, 1435, 1626, 2028, 2271 and 2467). All PDDs justify this based on the fact that the rate is more conservative, which is a controversial concept. This is because the lower rate overestimates

Table 2 Return indicators correlation table. IRR IRR NPV lnCAPEX lnOPEX Reductions kW

1.0000 0.3661 0.2183 0.0048 0.7272 0.6746

NPV 1.0000 0.6452 0.7224 0.1802 0.1221

lnCAPEX

lnOPEX

Reducts

kW

1.0000 0.7585 0.3279 0.3306

1.0000 0.2062 0.2489

1.0000 0.9785

1.0000

Table 1 Projects IRR mean and standard deviations. Mean std. dev.

Composting

Incineration

Brazil Chile China

4.74 –

5.34 0.95

Colombia India Indonesia

1.69 3.34 8.66 6.36

Israel Malaysia

(3.60) –

Mexico Thailand Viet Nam TOTAL

*

9.72 2.60 5.74 6.20

5.34 0.95

LFG

Manure

6.05 6.32 (2.22) 3.38 (0.19) 5.54 (2.07) 1.50 2.79 0.13 (0.26) 2.85 5.23 1.08 1.95 1.46 3.03 3.15 (1.95) 0.65 (0.80) – 1.13 5.21

NPV

RDF

Waste water*

NPV 1.50 6.12

9.81 –

1.79 4.39 5.43 – 5.98 0.21 3.35 6.31

3.71 5.47 NPV

3.49 – 3.21 5.85

6.59 2.38 NPV 5.98 0.21

4.32 4.96

Total 6.05 6.32 (2.22) 3.38 2.36 4.96 (0.20) 3.50 4.05 3.43 4.66 6.74 5.23 1.08 2.81 5.19 3.03 3.15 4.69 4.13 5.53 4.81 2.83 5.10

Wastewater type only.

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx

CER Prices 25 20 15 10 5 0 3.8 6 7.5 8 8.5 9 9.45 9.8 10.04 11.4 12 12.6 13 15 20 6 8 9 11 11.4 12 12.48 14 17 18.6 20.8

future positive cash flows. An atypical comparative case from one of the samples effectively illustrates this issue in Table 3. However, in these cases the net present value is never positive. Composting, incineration, gasification and refuse derived fuel use the benchmark analysis (IRR). Comparative analysis (NPV) is more common in manure (94%), 73 landfill (33%) and 17 wastewater projects (43%). This is illustrated by the sum of the 152 negative values of the projects that declare the NPV without CER, which exceed the negative 300 million dollar result (35% of total observations 1USD = 0.83EUR = 6.20CNY = 2.69BRL).

Num of projects

6

EUR

USD *few MYR, RMB, VND and INR occurrences translated or ignored

Source: Authors Fig. 3. Fixed CER prices declared at MDL projects⁄.

2.5. Revenues In the case of a registered CDM, a project has at least one source of financial revenue: the Certified Emission Reductions. However, while financial analysis is used to determine whether the project activity is (a) economically or financially attractive; or (b) economically or financially feasible without revenue from certificates (UNFCCC, 2004), CER income is not considered in project financial attractiveness. Other variables, such as fees, electricity, heat, compost, gas or the avoidance of fossil fuels for use in boilers and kilns, are considered. With respect to CER revenue, expected and ‘rights to sell’ contract prices are always fixed from US$6.00 to US$20.00 (4061), but the mode is US$10 and 8.0€. In very rare cases, the negotiable security prices were lower during the Post-Kyoto C1 period than initially estimated (e.g., 20% or 50%) (6797). Some projects also include the expected CER income total, but not the price per unit (6780, 6945, 7041 and 8064), and in at least four cases, CER income is not mentioned at all (6867, 7733, 8593 and 8751). No significant difference in value was found between countries, project types or over time. In fact, some projects estimated 18€ per metric ton of CO2 eq in 2012 (9303), and US$20.80 (9686) in 2013 (Fig. 3). The sample indicates that the intercept of the CERs-IRR linear function (accounting breakeven point) is between 7.43€ and 9.12€, and 9.68 and 15.72 U.S. dollars (p = 95%). Only one project declares its sensitivity to certificates prices, which with a value of US$5.50, reaches the benchmark. Indeed, many projects consider that the CER revenue will continue to be fixed over the project’s lifetime (21 years), and not only during the accreditation period (7–10 years). However, this depends on a new registration with new not considered costs (4611, 6771 and 6778). Using inappropriate values of CER income also distorts the IRR by as much as 80 percent (9253). The other revenues are not enough to competitively support the project’s economic attractiveness, mainly because of ‘‘lack of clear policy, regulation and direction associated with subsidies on fossil fuel, but not (or not adequate) on renewable energy’’, especially from (WTE) initiatives (3686). For example, electricity prices were not distinguished and were subsidized approximately 15–55% in Colombia (35€) (3332, 5402), and tariffs were 22 times lower (3.30€) than production costs in Cuba (2260). Nevertheless, many countries are developing distinct renewable energy tariff policies, which are typically conveyed in a national power grid system. For example, incinerators received approximately 35% of the electricity tariff in China for the first 15 years they were implemented (250YUAN/MW h) (5592, 5604, 5683,

Table 3 Project 5262 NPV discount rate comparison. Source: Israel Ports Landfill Project (5262). Discount rate (%) 10.5 15.5

Project NPV (1 MW) 11,797,356 12,172,436

Project NPV (2 MW) 10,822,934 12,497,090

5822 and 6134). The same type of differentiation can be observed after 2012 in major Annex 2 countries, such as FIRCO in Mexico (8508), ENCON in Thailand (8593) and Energy A3/2011 Auction in Brazil (8603). However, these incentives have some design flaws because waste projects compete with other renewable energy technologies. This still seems to not be sufficient for creating feasible projects, especially landfill gas initiatives, even under the most optimistic declared tariff settings. In the case of incinerators, composting and other anaerobic reactors of MSW projects, gate/tipping fees also play a crucial role in feasibility. However, they are ‘‘determined by the local government and unlikely to fluctuate dramatically during the years of operation’’ (8938). This indicates that in most countries, the MSW service cost is a local municipality duty related to fixed annual property taxes, and not related to volume in a modern 3R, or PAYT policy (Amaf et al., 2009; Ogawa, 2000). Governments directly, or by lowest cost based bid, manage the landfills, but not on the basis of tons of waste received. The attempts to change this policy were deemed unpopular, experience low payment rates, and unsuitably low fees (Lohri et al., 2014). A common practice in co-generation projects is to use shadow prices of a grid’s tariff of avoided electricity. This approach leads to higher prices because the values include electricity system administration and taxation (8751), or displaced fossil fuel consumption (coal diesel, natural gas) at boiler and kiln projects (3759, 4291 and 6988). On the other hand, the direct use of the upgraded biogas by the final gas customer, is a costly alternative, since the LFG should comply to technical purity and methane concentration (>99%) standards. Thus, only one project in Korea and another in Brazil formally declare that the main objective is to sell the methane to the grid under very specific conditions (0851, 9087). Projects that sell heat are also rare and are associated with a predefined market (1120, 1153 and 1653). Other than use by plantation projects owners, compost and fertilizer (manure, wastewater) suffer from trade issues, due to existence of cheaper substitutes and complementary products, a distrust in product quality and lack of an organized market to make their transactions, such as the one that exists in Pakistan (5460). 2.6. Capital and operational expenditures In most cases, expenditures are estimated thru spreadsheet income statements based on market and supplier prices. Few LFG electricity projects present US$/kWe rate (US$ 23.00–45.00) based OPEXs (0124, 1505, 1661, 1921, 3464, 4668). The technique, which is used by the World Bank, assumes that operational expenditures can be explained by a linear relationship per installed kW and scalability. An R-squared test (Table 4) shows that the relationship is significant, but particularly in the landfill industry, explains only half of the OPEX variation.

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx

7

Table 4 OPEX coefficients and R-squared utility indicators. Source: Authors. N  

Incinerator Landfill Manure Wastewater  

37 121 7 17

OPEX USD/kW *

33.742 34.468* 35.722* 22.453*

R2 OPEX

R2 lnOPEX

0.8433 0.5837 0.9906 0.9500

0.8376 0.5164 0.7180 0.7042

USD = 6,22CNY. p < 0.001.

*

We then test the hypothesis of gains of economy of scale in reference to size (financial) (Hf) and carbon reductions (environmental) (Hr). In reference to CAPEX, the OPEX relationship is significant (H0 – 0; t = 4.76), and CAPEX explains 85% of the OPEX variation. However, OPEX shows slight, but significant increases with CAPEX in all industries, which contradicts the financial economy of scale hypothesis (Fig. 4). In reference to carbon reductions, the correlation to OPEX-reductions is significant (t = 3.40). However, results do not reject the null hypothesis, indicating that the relationship between reductions and size is ‘‘designed’’ linear, with no gain of scale. 2.7. Sensitivity analysis A sensitivity analysis is a technique that varies particular values to verify the impact on a chosen financial indicator (NPV or IRR). It is especially useful for WM project’s quantitative analysis like CBA, risk-based multi-criteria assessment (RBMCA), modeling and analyzing decision-making in situations with multiple stakeholders, including for regulatory purpose (Karmperis et al., 2012, 2013). Since these novel approaches are very recent, no project uses formal probability functions or calculates any alternative’s score, and the assessment is quite linear and arbitrary. This type of analysis is not used for projects that have no revenue, in which the baseline scenario is unique or the cost analysis is indicated, such as gas flare, leachate evaporation and some co-generation projects. The most common used variables are the tariff and production of electricity, capital and operational costs, avoided fossil fuel price (cost savings) and fertilizer and compost prices. The most commons variations are +10% and 10%, but can reach 35%. Since 2009, projects are commonly utilizing reverse sensitivity analyses, which verify how much a variable must be altered to project return meet benchmark (since project 1745). However, all projects warn

Fig. 5. Sensitivity analysis most common scenario.

that variations such as greater than 20% are unlikely, and nearly impossible. The rule is that products (electricity tariff, compost or displaced fuel prices) affect evaluation indicators more so than cost (Fig. 5). The graph central point is the original IRR. The horizontal axis depicts the relative change of each variable. Labels and vertical axis present the resultant IRR. Per example, a +10% variation in Tipping Fee (light blue line) results in a 6.35% IRR increase. Other evidence is that smaller tariff variations are necessary to project return reach benchmarks when using reverse sensitivity analyses (6320, 8851). In addition, investment variations (2271, 3248, 3820) are more relevant in projects that are capitally intensive, such as incinerators (3480, 3525, 3694, 3837, 4824 and 5822), or that are comparatively significant to the total investment, such as composting. At incineration projects, the amplitudes of the return variations of optimistic and pessimistic scenarios are more than 3 times smaller than other industries. In fact, considering a t statistic with 95% confidence, incineration is the only industry with more than one occurrence when the lower limit of the IRR pessimistic scenario is positive. Both statistic values indicate that incineration is the least risky sector of waste management, which, in turn, indicates the benefits of information uniformity (Table 5). Nevertheless, there are a few cases in which the variation allows the indicator to reach the benchmark measure without CER revenue (1853, 2271, 3480, 3837, 5842, 6348 and 6680), ‘‘even under more favorable conditions’’ (5105). Indeed, there are two cases, in which, even with CERs revenues, the IRR does not reach the benchmark (2816, 8066).

.6

.8

1

1.2

3. Results and discussion

.4

Proportional OPEX (ln)

Source: Project 5683 - Xingou MSW Power Generation Project

10

15

20

CAPEX (ln)

Source: Authors Fig. 4. Proportional OPEX/CAPEX scatter plot.

25

The Kyoto credit scheme developed by the UNFCCC attempted to, in principle, provide a more economically efficient way to meet targets by reducing the costs of obligated parties and, in the same way, provide funds to developing countries. However, marketable pollution permit schemes will work since, and only since, ‘‘polluters have different costs of abatement’’ (Pearce and Turner, 1990, p. 112). Therefore, the balance is overestimated, and the EU ETS, and derivative AAUs and CERs, are currently trading at the bottom end of the range. However, ‘‘in the past and looking ahead, market expects Kyoto offers a significant incentive for attracting investments’’ (Barton et al., 2008). Meanwhile, in addition to mitigating carbon CDM, parties will have to mitigate financial losses. Thus, the situation favors the construction and pursuit of a self-sufficient environment for new investment, and realization of planned project upgrades (engines, boilers, turbines, etc.). Converging benchmark results corroborate with the increasing integration, stability, interdependence and competition between

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Table 5 Internal rate of return project scenarios and amplitude. Source: Authors. Industry

Internal rate of return Obs.

Biomass Composting Gasification Incineration Landfill Manure RDF Sewage Sludge Wastewater Total

3 20 1 39 252 60 3 2 51 431

Optimistic

Pessimistic

Average

St. dev.

6.44 8.57 12.12 6.76 6.05 8.33 12.83 5.19 8.63 6.78

4.09 5.31 NA 1.03 5.79 2.96 1.64 NA 4.99 5.11

Confidence limits (t)

Average 2.31 0.50 3.51 3.67 2.03 2.09 2.22 3.51 1.73 0.09

countries, as well as with waste management industry maturation. The geographical distribution of projects suggests a block behavior, with some neighborhood influence. However, only one case had a projected positive financial result (NPV). The rest of the projects have negative NPVs, and do not meet the benchmarks. Indeed, this work estimates that, without CER revenue, the sum of negative results exceeds a half billion U.S. dollars. These results vary within the industries and countries, but only LFG projects exhibit negative IRR averages. The standard deviation is also greatly influenced by the combination of host, industry and project consultants, suggesting that uniformity reduces planning variances (risk concept in finance).

St. dev

Inferior 95%

Superior 95%

2.47 8.18 NA 1.74 6.50 6.14 0.89 NA 5.69 6.13

5.55 17.55 NA 0.15 14.83 14.37 5.03 NA 9.69 NA

19.44 19.65 NA 8.85 17.46 14.25 18.05 NA 18.64 NA

Because of their capacity to influence results, revenues are the more significant factor. In addition, because CER revenue is always overestimated and out of the time range, and the projects are not attractive without it, or the parties inevitably will absorb the losses, or the project must cut investments and expenses. The results indicate that the implementation of economic instruments beyond costs and capital incentives, such as electricity/heat tariff differentiation, displaced fossil fuel subsides and an adequate tipping fee, would greatly help. However, results from a sensitivity analysis of revenue suggest that a factor would need vary significantly, which is extremely improbably, to change the attractiveness of an evaluation.

Table A.1 Projects host, reductions and type +2 countries. Source: Authors, based on UNFCCC (2014). Hosts China Brazil Mexico Thailand Indonesia Chile Malaysia Colombia Viet Nam India Israel Argentina South Africa Philippines Ecuador Peru Guatemala United Arab Emirates Costa Rica Azerbaijan Egypt Pakistan Cameroon Republic of Korea Jordan Saudi Arabia Bangladesh Syrian Arab Republic Armenia Tunisia Morocco Côte d‘Ivoire Nicaragua Nigeria Less than 2 TOTAL Reductions *

Composting 1

Incinerator 37 1

5 1 2 2 3

1

1 1 1

1

LFG 46 49 28 5 9 15 8 11 5 3 9 9 6 4 2 3 1 2 2 1 1 1 2 2 2 2 1 2 1 2 1 2

Manure

RDF

4 20 22

6

18 6

1 5

1 1 2

Wastewater biomass sludge

3

10 1 4 1

1 1 3 1

1 1 2

1 1 20 1.72

39 4.89

1 14 252 46.34

60 4.72

3 0.28

4 56 5.46

Total

Reductions (Mt)

94 70 50 23 21 20 19 14 12 11 11 9 7 6 5 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 19 430* 63.41

13.14 13.56 4.72 2.30 2.14 2.52 1.69 3.30 1.92 1.25 1.14 3.26 1.67 1.14 0.32 0.43 0.24 0.31 0.19 0.15 0.40 0.21 0.19 1.62 0.25 0.49 0.17 0.13 0.20 0.69 0.18 0.59 0.21 0.41 2.28 63.41 63.54*

+1 Sri Lanka gasification project (0.13Mt reductions).

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx Table B.1 CDM Projects cited. Source: Authors. Ref

Project title

Host parties

0008 0052 0053 0096

Brazil Brazil Mexico Chile

1240 1242 1301 1307 1355 1435 1505 1626 1653 1661 1737 1745 1853 1921

Brazil NovaGerar Landfill Gas to Energy Project Salvador da Bahia Landfill Gas Management Project AWMS GHG Mitigation Project MX05-B-16, Sinaloa and Sonora, México Copiulemu landfill gas project (Center for the Storage and Transfer, Recovery and Control of Waste, Treatment and Disposal of Industrial and Household Waste) Methane Extraction and Fuel Conservation Project at Tamil Nadu Newsprint and Paper Limited (TNPL), Kagithapuram, Karur District, Tamil Nadu Bandeirantes Landfill Gas to Energy Project (BLFGE) Composting of Organic Waste in Dhaka São João Landfill Gas to Energy Project (SJ) Loma Los Colorados Landfill Gas Project Daegu Bangcheon-Ri Landfill Gas CDM Project Quitaúna Landfill Gas Project (QLGP) PT Navigat Organic Energy Indonesia Integrated Solid Waste Management (GALFAD) Project in Bali, Indonesia Sudokwon Landfill Gas Electricity Generation Project (50 MW) Jiaozishan Landfill Gas Recovery and Utilisation Project Ciudad Juarez Landfill Gas to Energy Project Methane recovery and utilisation project at United Plantations Berhad, Jendarata Palm Oil Mill, Malaysia PT. BUDI ACID JAYA Tapioca Starch Production Facilities Effluent Methane Extraction And On-site Power Generation Project in Lampung Province, Republic of Indonesia Hasars Landfill Gas Project Tultitlan – EcoMethane Landfill Gas to Energy Project Methane Recovery and Utilization CDM Project at Muyuan Swine Farm in Henan Province Durango – EcoMethane Landfill Gas to Energy Project Retamim Landfill Project Regional landfill projects in Chile Nanning Landfill Gas to Energy Project Feira de Santana Landfill Gas Project TTY Cambodia Biogas Project Kunming – Wuhua Landfill Gas to Energy Project Methane Recovery for Onsite Utilisation Project at Desa Kim Loong Palm Oil Mill, Sook, Keningau, Sabah, Malaysia Nanchang Maiyuan Landfill Gas Recovery and Utilisation Project Montalban Landfill Methane Recovery and Power Generation Project Durban Landfill-Gas Bisasar Road

1925 2028 2076 2110 2138 2144 2148 2180 2181 2185 2260 2271 2374 2378 2427 2467 2487 2518 2554 2751 2778 2785 2816 2867 2970 3042 3248 3313 3332 3370 3464 3480 3483 3525 3643 3677

Taman Beringin Integrated Landfill Management Project, Kuala Lumpur, Malaysia Methane capture and destruction on La Hormiga landfill in San Felipe and El Belloto landfill in Quilpue Bundle CDM project Univanich Lamthap POME Biogas Project Cassava Waste To Energy Project, Kalasin, Thailand (CWTE project) Chao Khun Agro Biogas Energy Project Jiratpattana Biogas Energy Project Chumporn applied biogas technology for advanced waste water management Bioenergia Anaerobic Digestion and Biogas Generation Project Methane Capture and On-site Power Generation Project at Syarikat Cahaya Muda Perak (Oil Mill) Sdn. Bhd. in Tapah, Perak, Malaysia Methane Capture and On-site Power Generation Project at Sungai Kerang Palm Oil Mill in Sitiawan, Perak, Malaysia Methane capture and destruction on Calle 100 landfill in Havana and Gascon landfill in Santiago de Cuba. Bundle CDM project Tecamac – EcoMethane Landfill Gas to Energy Project Municipal Solid Waste (MSW) Composting Project in Urumqi, China Integrated Municipal Waste Processing Complex at Ghazipur, Delhi Inno-Kwants Mewah – Palm Oil Mill Waste Recycle Scheme, Malaysia Landfill Gas Recovery and Utilization at Bukit Tagar Sanitary Landfill, Hulu Selangor in Malaysia Reduction of Methane Emissions from Ruseifeh Landfill Gikoko-Makassar – LFG Flaring Project Doña Juana landfill gas-to-energy project Piyungan Landfill Gas Capture Project in Yogyakarta Composting of Organic Content of Municipal Solid Waste in Lahore Proyecto Multiambiente del Plata Norte IIIa Hunan Loudi Miaopu Landfill Gas to Power Project Installation of Bundled Composting Project in the state of Tamil Nadu TBEC Tha Chang Biogas Project Dehydration and incineration of sewage sludge in Singapore Bundled Waste Processing Facilities in India Methane Reduction at the Taibe’e Landfill using In-situ Aeration Bionersis LFG project Colombia 2 Amman Ghabawi Landfill Gas to Energy Project Exploitation of the biogas from Controlled Landfill in Solid Waste Management Central – CTRS/BR.040 Hanyang Municipal Solid Waste Incineration for Energy Generation Project in Haining City Bangkok Kamphaeng Saen West: Landfill Gas to Electricity Project Huzhou Municipal Solid Waste Incineration for Power Generation Project Proactiva Tlalnepantla Landfill Gas to Energy project Ekurhuleni Landfill Gas Recovery Project – South Africa

3686 3694

Sungei Kahang POME Biogas Recovery for Energy Project in Johor, Malaysia Yangzhou City MSW Incineration Power Generation Project

0124 0164 0169 0373 0822 0851 0912 0938 0941 1120 1123 1153 1176

India Brazil Bangladesh Brazil Chile Korea Brazil Indonesia Korea China Mexico Malaysia Indonesia Mexico Mexico China Mexico Israel Chile China Brazil Cambodia China Malaysia China Philippines South Africa Malaysia Chile Thailand Thailand Thailand Thailand Thailand Guatemala Malaysia Malaysia Cuba Mexico China India Malaysia Malaysia Jordan Indonesia Colombia Indonesia Pakistan Argentina China India Thailand Singapore India Israel Colombia Jordan Brazil China Thailand China Mexico South Africa Malaysia China

(continued on next page)

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx

Table B.1 (continued) Ref

Project title

Host parties

3716 3759 3820 3837 3958 4061 4155

China China Israel China Brazil Indonesia Thailand

4175 4211 4265 4291 4316 4379 4598 4611 4668 4678 4682 4824 5105 5194 5326 5364 5402 5434 5460 5518 5556 5592 5604 5619 5683 5692

Beijing Deqingyuan Chicken Farm 2.4 MW Biogas Power Project Methane Recovery Project of Jiangsu Lianhai Bioengineering Co., Ltd. Evlayim Landfill Project Chengdu Luodai Municipal Solid Waste Incineration Project CTR Candeias Landfill Gas Project Organic Waste Composting at CKT Palm Oil Mill, Indonesia Eiamheng Tapioca Starch Industry Co.,Ltd. Tapioca starch wastewater biogas extraction and utilization project, Nakhonratchasima Province, Kingdom of Thailand Douala Landfill gas recovery and flaring project Manaus Landfill Gas Project BAJ Tulang Bawang Factory tapioca starch wastewater biogas extraction and utilization project, Lampung Province, Republic of Indonesia Methane Recovery and Utilization Project of Dai Viet Co. Ltd, Vietnam Bionersis LFG Project Malaysia (Penang) Hutama Green Energy Methane Capture and Utilization Project at Starch Tapioca Bandar Mataram, Central Lampung, Indonesia Monterrey I LFG to Energy Project Avoided emissions from biomass wastes through use as feed stock in pulp and paper Kunak, Sabah production i.e., Eko Pulp and Paper Project Changchun City Landfill Gas Power Generation Project Methane Emission Utilization for Power Generation from Ethanol wastewater treatment at PT. Indonesia Ethanol, Lampung province, Indonesia Los Mangos landfill gas capture and flaring project Changshu Municipal Solid Waste Incineration Project Waste to Energy Project of SURE VN in Binh Duong Province, Viet Nam Dalian Xiajiahe Sludge Treatment Project in Dalian City, People’s Republic of China Hunan Changsha Qiaoyi Landfill Gas Recovery and Electricity Generation Project Wastewater Treatment and Methane Recovery at Green Field Joint Stock Company La Glorita Landfill Gas Project Marrakesh Wastewater Treatment Plant (WWTP) with biogas recovery for cogeneration Compost from Municipal Solid Waste in Peshawar, Pakistan VG Energy’s Waste to Power at Vichitbhan Palmoil Co., Ltd. Avoided methane emission through aerobic composting at Vietstar municipal solid waste treatment facility Yuhuan MSW Incineration for Power Project Yongkang MSW Incineration for Power Project Modelo del Callao Landfill Gas Capture and Flaring System Wuhan Jiangbei West (Xingou) Municipal Solid Waste (MSW) Power Generation Project Nelson Mandela Bay Metropolitan’s Landfill Gas Project

5822 5828 5842 5861 5999 6039 6073 6134 6241 6252 6254 6348 6363 6486 6565 6680 6699 6771 6778 6780 6797

Xiamen Eastern Municipal Solid Waste Incineration Project Hui’an MSW Incineration Project Biogas production from sugar beet press pulp Südzucker Moldova sugar plant Norte III.C landfill – Methane recovery and power generation project Eco Energy Beer Tuvya – Animal manure anaerobic treatment facility Land Filling and Processing Services for Southern Zone in Cairo Ningbo Yinzhou Landfill Gas Recovery and Utilization Project Hebei Lingda Municipal Solid Waste Incineration Project Advanced Wastewater Management at Rajburi Ethanol Plant Israel Ports Landfill Project Municipal Solid Waste Anaerobic Digestion with Gas Collection and Power Generation Project in Jiaonan City, P.R. China Laogang Landfill Gas Recovery and Utilization Project Bionersis LFG Project Chile 4 (Los Angeles) Bantargebang Landfill Gas Management & Power Generation KI Biogas Co., Ltd. Wastewater Treatment for Energy Generation, Nakhon Ratchasima Cu Chi Municipal Solid Waste (MSW) Treatment Plant in Ho Chi Minh City, Vietnam Project DSK Composting Project García Landfill Gas Project Ahome Landfill Gas Project Chengdu Xiangfu Municipal Solid Waste Incineration for Power Generation Project Joburg Landfill Gas to Energy Project

6804 6867 6945 6988 7018 7041 7262

Proactiva Presidente landfill gas to energy project Querétaro landfill-gas-to-energy project Shenzhen Baoan Laohukeng Stage II Municipal Solid Waste Incineration Project Industrial Wastewater Methane Recovery Project of Bengbu Tushan Thermoelectricity Co., Ltd. 7.5 MW Poultry Litter Project by Redan Infrastructure Private limited Mudanjiang Guojiagou Landfill Gas Power Generation Project Avoidance of methane emissions from the wastewater treatment facility and renewable energy generation at Eiam E-San Renewable Co., Ltd. in Thailand Natal Landfill Gas to Energy Project Baku Waste to Energy Project Proactiva Nuevo Laredo landfill-gas-to-energy project Efe’e Landfill Gas to renewable electricity Project Power generation through MSW at Karimnagar, Andhra Pradesh Kaifeng Municipal Solid Waste Incineration for Power Generation Project Qingdao MSW Incineration Power Generation Project TBEC LIG Biogas Project Barueri Energy CDM Project Activity

7637 7658 7733 7777 7790 8064 8066 8126 8128

Cameroon Brazil Indonesia Viet Nam Malaysia Indonesia Mexico Malaysia China Indonesia Costa Rica China Viet Nam China China Viet Nam Colombia Morocco Pakistan Thailand Viet Nam China China Peru China South Africa China China Moldova Argentina Israel Egypt China China Thailand Israel China China Chile Indonesia Thailand Viet Nam Philippines Mexico Mexico China South Africa Colombia Mexico China China India China Thailand Brazil Azerbaijan Mexico Israel India China China Lao PDR Brazil

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A.L. Bufoni et al. / Waste Management xxx (2015) xxx–xxx Table B.1 (continued) Ref

Project title

Host parties

8146 8181 8205 8242 8360 8369

Mahkota Andalan Sawit Composting Project Balakhani Landfill Project CGR CATANDUVA LANDFILL GAS PROJECT CTR Rosario Landfill Gas Project Cadereyta Landfill Gas Project Manufacture and utilization of bio-coal briquettes in Stutterheim, South Africa

8508 8593 8603 8751 8851 8874 8938 8980 9045 9087 9104 9115 9245 9253 9272 9303 9413 9686

AWMS Methane capture and electricity generation project in PROAN farms, Jalisco Thai Roong Ruang Energy Wastewater Treatment and Biogas Utilization Project Constroeste Landfill Gas to Energy Project Proactiva CGA Iperó Landfill Gas to Energy Project The Colomba Guabal Landfill Gas Project Methane Recovery and Utilization Project of Petrovietnam Biofuels Joint Stock Company Ningde Municipal Solid Waste Incineration and Power Generation Project Green power Landfill Gas Project Sapthip Wastewater Management and Methane Capturing for Heating and Electricity generation Gramacho Landfill Gas Project Municipal Solid Waste to Energy Project by Western Power Company (Pvt) Ltd PT Medco Ethanol Lampung wastewater treatment and biogas utilization project Kilang Minyak Sawit Tg. Tualang Mill Wastewater Biogas Recovery and Utilisation Project Gas Collection, Incineration and Electricity Generation System at Da Phuoc Integrated Waste Management Facility Integrated Solid Waste Management Project at Mathura, Uttar Pradesh Zone 3 Landfill Gas Project LFG management project for second phase of Liulitun landfill in Haidian District Puerto Chivos Landfill Gas Project

Indonesia Azerbaijan Brazil Brazil Mexico South Africa Mexico Thailand Brazil Brazil Colombia Viet Nam China Israel Thailand Brazil Sri Lanka Indonesia Malaysia Viet Nam India Guatemala China Mexico

Different correlations of emissions and energy production from landfill projects, as well as capital and operational expenses, may suggest an additional and disproportional gas collection system expenses. In addition, it emphasizes the importance of a better tipping fee system, and gas production planning uncertainties, which can result in an underestimation of the amount of LFG production needed to avoid idle capacity. Contradictory results from financial economy of scale hypotheses also point to inaccuracies related to size. Cumulative errors overestimating and underestimating values creates significant uncertainties that greatly reduce the utility and meaning of the reports and spreadsheets. The results of sensitivity analyses indicate that revenues are more cost-benefit effective than capital and operational cost reductions, but no isolated policy is capable of solving the problem of economic waste management feasibility, suggesting that the presented variables should be treated as coordinated bottleneck regulation barriers. The results also reinforce the verification that information uniformity and completeness can reduce sector benchmark and capital expenses. Because this work only considers mutually exclusive, one-dimensional input variation in sensitivity analyses, future research could estimate the effects of an adequate combination of tariff-cost-investment subsidies or directives, which more efficient and at lower economic cost, to make waste management projects more attractive. Future researches could revisit planning and design documents, confronting them with the performance reports, investigating the shortcomings and possible improvements of the budgeting and of the operational risks. Of course, this suggestion is difficult to operationalize, since a robust and joint database to proceed the analysis does not exist. 4. Conclusions This study concludes that simple modifications to waste management financial assessments could greatly improve predictive and ex-post utility of the planning, budgeting and results statements. International financial standards and report templates would improve the desired qualitative characteristics of financial statements (understandability, relevance, comparability and reliability). Waste management financial information is difficult to find and compile. Assembling the data sampled in this paper was time

consuming and incomplete. In part, implementing standards can reduce missing information, but this work concludes that an extensive open source database with key parameters would be very helpful for project design decision making and research, allowing a better and more reliable compilation and comparison of data, thereby reducing uncertainties and risk, and fostering a lower sector benchmark rate. Municipal solid waste sample descriptions indicate that the use of landfill gas to generate electricity is utilized in almost all countries, although the waste literature and directives recommend incinerators and digesters, to reduce landfilling (European Council, 1999; Barton et al., 2008; EPA, 2011; MMA, 2012). In addition, financial results and analyses suggest greater inaccuracies and uncertainties related to LFG than to other technologies. For now, this article concludes that LFG is an economically efficient solution, but significant uncertainties exist with the currently used tools and insufficient data. These uncertainties impacted the business plan parameters in a conservative way and that, in turn, suggest that LFG is a less economically efficient solution. A future comparative study with other technologies like AD and the incinerators could investigate the origin of such difference, verifying the externalities in a comprehensive economic way, considering, per example, landfill cover fugitive emissions and its baseline methodology, post-closure expenditures, land hedonic value, increasing site distance and transportation costs and lifetime. Furthermore, a financial data analysis indicated that a clearer policy on renewable energy and fuel prices, associated with long term and adequate subsidies and capital expenditures, would be more financially effective in reducing costs than would subsidies. However, the regulatory environment should not favor a single factor because multiple factors are needed to make the project attractive. More financial information is presented in each project’s Validation Report at UNFCCC/CDM website (UNFCCC, 2014). The accordant document reexamines the financial parameters individually, nevertheless no PDD’s adjusted financial statement is available. In addition, after that parameters are adjusted, no periodic consolidated statement is accessible. Since all this information is presented separately and it refers specifically to project’s

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Please cite this article in press as: Bufoni, A.L., et al. The financial attractiveness assessment of large waste management projects registered as clean development mechanism. Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.06.030