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Risk and Capital Requirements for Infrastructure Investment in Emerging Market and Developing Economies

Joaquim Levy

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Mobilizing private investment in infrastructure will be key to increase growth and resilience in developing countries. Well-planned infrastructure can raise potential output growth and help reduce the carbon footprint of progress. Directing excess savings from advanced economies towards emerging market and developing economies (EMDEs) helps address the low investment returns of institutional investors in developed economies while supporting achieving the Sustainable Development Goals (SDGs) by 2030.

Infrastructure is a natural match for insurers’ long-term liabilities. Long-term fixed income instruments fit well with the long-dated liabilities of insurance companies, especially for those offering life insurance and annuity products. Infrastructure projects tend to yield long-term, predictable cash flows, with low correlation to other assets and relatively high recovery value in case of repayment arrears. This match is so significant that some regulators provide special treatment for insurers that hold them to maturity. The recent update of Europe’s Solvency II Directive, for instance, provides for a “matching adjustment” that allows insurers to discount their liabilities by the rate of return of infrastructure-linked instruments, which tends to be higher than the market-implied discount rates, thus reducing the present value of these liabilities and the business cost for insurers.

However, insurance companies still allocate less than 2.5 percent of assets under management to infrastructure investment, in part because of insufficient understanding of the risk profile of this asset class. There are many reasons for the low participation of infrastructure, including the limited supply of fully operational infrastructure projects issuing debt. There is also an informational hurdle, with investors’ perception of infrastructure being risky, despite the long tradition of regulated utilities of yielding low-risk cash flows. This perception is also reflected in some regulatory frameworks, which require insurers to allocate sizeable amounts of capital to support investments in long-term debt, especially for unrated transactions, thus reducing the internal rate of return and the profitability of holding these instruments.

More recently, European regulators have acknowledged the particular risk properties of infrastructure, reducing the capital charge on this type of finance. Following the advice of the European Insurance and Occupational Pension Authority (EIOPA), which performed a comprehensive analysis of historical data of infrastructure risks in advanced economies, the European Commission in September 2016 revised down the standard formula for capital charges on qualifying infrastructure debt (and equity) investments under the Solvency II Directive. This calibration resulted in a significant relief of infrastructure debt relative to equivalent corporate bonds and loans. However, this more favorable regulatory treatment remains restricted to investments in countries that are members of either the European Economic Area (EEA) or the Organization for Economic Co-operation and Development (OECD). So, infrastructure projects in many EMDEs do not benefit from it.

New empirical analysis of infrastructure debt in EMDEs offers an opportunity to widen the perimeter of a more favorable regulatory treatment. Recently, Moody’s Investor Service published a detailed analysis of the historical credit performance of project finance bank loans, which account for 80 percent of the funding of project finance transactions originated globally since January 1, 1983. The study reviewed data of more than 6,000 projects from a consortium of leading sector lenders (Moody’s Project Loan Data Consortium), of which more than 1,000 are projects in EMDEs.

The study shows that credit performance of project loans in EMDE debt is not substantially different from that of comparable debt in advanced economies. As in advanced economies, the risk profile of project bank loans in EMDEs improves over time. Specifically, the marginal default rate–i.e., the likelihood that an infrastructure loan performing at the start of a specific year will default within that year–exceeds the level for non-investment grade corporate exposures by the time of the financial closing of the project, but it steadily declines as the loans mature, when projects reach “brownfield stage”. Cumulative default rates of infrastructure become flat like those of investment grade instruments, while rates for originally equivalent corporate debt continue to rise throughout their lives (Figure 1).  After five years, the marginal default rate of project loans is consistent with that of “AA/Aa”-rated corporates and, actually, on average lower in EMDEs than in advanced economies. For PPPs, the cumulative rate of return over the first 10 years of project loans in EMDEs is virtually the same as those in advanced economies, at less than 6 percent. Also, recovery rates for EMDE project loans average about 80 percent, and, thus, are like those for senior secured corporate bank loans.

Figure 1: Cumulative Default Probability of Unrated Project Loans in Advanced and Developing Economies

Sources: Moody’s Investors Service (2017) and Jobst (forthcoming). Note: based on the shortened study period between 1995 and 2015; the sub-samples “EEA or OECD,” “EMDE-A” and “EMDE-B” correspond to the samples selected in the Moody’s report and cover EEA and OECD member countries, all non-high income countries, and all non-high income countries without EEA or OECD members (i.e., Bulgaria, Croatia, Mexico, Romania, and Turkey).

Applying the relevant data from the recent Moody’s report to two important solvency regimes for insurers shows sufficient scope for reducing the capital charge for investments in infrastructure debt. World Bank staff in the finance area have recovered the credit risk parameters from the published  data on project loans and applied them to the relevant elements of the Solvency II Directive and the International Capital Standard (ICS) for internationally active insurers, which will be implemented by the International Association of Insurance Supervisors (IAIS). We apply these data to these solvency regimes, differentiating the properties of infrastructure loans from the standard corporate exposures without adjustments to current regulatory methodologies. Only the intrinsic risk profile of infrastructure debt vis-à-vis the standard risk assumptions on long-term debt was considered. When doing so, we find that the capital charges would decline significantly when these differences in risk are considered. Specifically, for a 10-year risk horizon, the annual expected loss of project finance loans (1.6 percent) is half of the expected losses implied by “Ba/BB”-rated non-financial corporates, and the implied capital charges would decline from 23.5 to 13.3 percent under Solvency II (Table 1). Under ICS, it would drop from 12.7 to 10.7 percent, consistent with the estimated economic capital within the range of 10.5 to 13.8 percent (based on the 99.5 percent conditional tail expectation).  Additional analysis of rated EMDE infrastructure debt securities, using data from another Moody’s Investors Service report published earlier in 2017, indicates some flexibility to lower capital charges on these instruments under Solvency II. For instance, the charge for “Baa/BBB”-rated securities, would come down from 20 percent to about 16 percent.

Table 1: Credit Risk and Estimated Capital Charges for Unrated Project Loans (using standard risk parameters and differentiated infrastructure risk profile) *

Sources: BCBS (2017), European Commission (2015 and 2017), IAIS (2017), Moody’s Investors Service (2017) and Jobst (forthcoming). Note: recovery rate refers to ultimate recovery rate; */calculated over 10-year horizon with recovery rate consistent with unsecured senior claims; **/ reduced capital charge if qualifying infrastructure exposure in EEA or OECD country; 1/ fixed risk factors of the Solvency II SCR Standard Formula — Spread Risk Sub-Module for fixed income investment, as amended by Regulation (EU) 2015/35 (October 10, 2014) and EU Regulation 2017/1542 (June 8, 2017); 2/ credit risk factor under the proposed International Capital Standard (ICS) is assumed to follow the advanced internal ratings-based (A-IRB) approach for specialized lending (project finance) using the cumulative PD with/without a floor for PD and LGD and full application of the maturity adjustment; 3/ based on credit risk (PD and LGD) of global non-financial corporate debt issuers; 4/ based on 99.5% conditional tail expectation (CTE).

Even a modest reduction in capital requirements for long-term infrastructure investments can significantly boost return-on-equity (RoE) under a prudent but differentiated regulatory treatment. For instance, considering a stylizing illustration for a European regulated insurer holding a 10-year infrastructure loan yielding 4.6 percent annually (less the insurer’s borrowing cost of 1.0 percent and an income tax rate of 35 percent), reducing the capital charge of 23.5 percent (under the current standard formula approach applied to corporate exposures) to about 14 percent (under a differentiated approach) would raise the RoE of investing in such an instrument from 10 percent to more than 17 percent. The latter figure is more than 50 percent above the average RoE of European life insurers in 2016.

Figure 2.  Return on Equity of Infrastructure Debt Investment as a Function of Regulatory Capital Charges

Sources: Bloomberg L.P., Moody’s Investors Service (2017) and Jobst (forthcoming). Note: The calculation is based on the annual yield (less the risk-free rate of 1.0 percent) after tax (35 percent); 10-year U.S. government debt yield at 2.31 percent as of end-Sept. 2017 and median RoE of European life insurers as of mid-2016 (EIOPA, 2017); 1/ average infrastructure loan rate in the U.K. (4.3 percent) according to Institute and Faculty of Actuaries (2015) at end-2014 and scaled to EMDE consistent with infrastructure bonds (4.6 percent); 2/ based on the Solvency II Spread Risk Sub-Module (European Commission, 2015, 2016 and 2017), assuming unrated exposure is treated like corporate exposure (loans/bonds) with credit quality step (CQS) of 5 (‘B’) and assumed maturity of 10 years (OECD, 2015).

Lower capital charges can help maximize finance for development, unlocking an important source of long-term capital for global growth. Although regulatory disincentives for infrastructure investment in EMDEs may be just one of the impediments to growing exposure to this asset class, the evolution of these regulations can be an important step forward.  By helping to increase the rate of return of holding infrastructure-linked instruments potentially by up to 50 percent, it may help insurers and other institutional investors to accelerate the rebalancing of their assets in ways that will help crowd-in resources in quality climate-smart infrastructure projects in EMDEs.  These projects are an important part of strategies to increase the resilience of these economies while helping eliminate extreme poverty and produce shared prosperity.

First published in World Bank

Note: The capital charges computed here were reached by using the implied transition probabilities for infrastructure loans and  (i) mapping the current reduction factors for qualifying (unrated) infrastructure investment for EEA/OECD countries under the Solvency II SCR Standard Formula — Spread Risk Sub-Module to the expected loss of project loans in EMDEs and (ii) calibrating expected loss to the credit risk stress factor for ICS (IAIS, 2017) following the advanced internal ratings-based approach according to the finalized Basel III framework. For details, refer to Jobst, Andreas A., forthcoming, “Credit Risk Dynamics of Infrastructure Investment—Considerations for Insurance Regulation,” Working Paper (Washington, D.C.: World Bank Group).

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Economy

Banks and Artificial Intelligence

Giancarlo Elia Valori

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“Artificial Intelligence” is a terminology specifically invented in 1956 by John McCarthy and concerns the ability to make appropriate generalizations quickly, but based on an inevitably limited set of data.

 The wider the scope and the faster conclusions are drawn, and with minimal information, the smarter the machine’s behaviour can be defined.

 Intelligence is the creative adaptation to quick changes in the environment. This is the now classic definition, but in this case, with machines, the speed and the increasingly narrow base of the starting data are also evaluated.

 What if the starting data does not contain exactly the necessary information – which is possible? What if, again, the speed of the solution stems from the fact that the data collected is too homogeneous and does not contain the most interesting data?

 Konrad Lorenz, the founder of animal ethology, was always very careful to maintain that between instinctive behaviour and learned behaviour, external environmental and genetic sources can be equally “intelligent”. The fact, however, is that greater flexibility of a behaviour – always within a reasonable time, but not as quickly as possible – generates greater intelligence of the animal.

As said by a great student of Lorenz, Nikko Tinbergen, human beings are “representational magpies”, which means that much of their genetic and informative history has no practical value.

 When the collection of information becomes easy, the “adaptive” magpie has a very adaptive behaviour, but when the data collection is at the maximum, all data counts and we never know which, among this data, will really be put into action.

 In other words, machine data processing is a “competence without understanding”, unless machines are given all senses – which is currently possible.

 Human intelligence is defined when we are at the extreme of physically possible data acquisition, i.e. when individuals learn adaptive-innovative behaviour from direct imitation of abstract rules.

 Abstract rules, not random environmental signals.

 If machines could reach this level, they would need such a degree of freedom of expression that, today, no machine can reach, not least because no one knows how to reach this level; and how this behaviour is subsequently coded.

 What if it cannot be encoded in any way?

 The standardization of “if-then” operations that could mimic instincts, and of finalized operations (which could appear as an acquired Lorenz-style imprinting) is only a quantitative expansion of what we call “intelligence”, but it does not change its nature, which always comes after the particular human link between instinct, intelligence and learning by doing.

 Which always has an accidental, statistical and unpredictable basis. Which duck will be the first to call Konrad Lorenz “dad”, thus creating a conditioning for the others? No one can predict that.

If systematized, bio-imitation could be a way to produce – in the future – sentient machines that can create their own unique and unrepeatable intelligent way to react to the environment, thus creating a one and only intelligent behaviour. Will it be unique?

However, let us go back to Artificial Intelligence machines and how they work.

 In the 1980s there was the first phase of large investment in AI, with the British Alvey Program; the U.S. DARPA Program spending a billion US dollars on its Strategic Computing Initiative alone; finally the Japanese Fifth Generation Computer Project, investing a similar amount of money.

At the time there was the booming of “expert systems”, i.e. symbolic mechanisms that solved problems, but in a previously defined area.

From the beginning, expert systems were used in financial trading.

There was the hand of the expert system in the fall of the Dow Jones Industrial Average by 508 points in 1987. In 1990, however, Artificial Intelligence also began to be used in the analysis of financial frauds, with an ad hoc program used by the Financial Crimes Enforcement Network (FinCEN), especially with the possibility to automatically review 200,000 transactions per week and to identify over 400 illegal transactions.

Machine learning, the model on which the most widely used AI financial technology relies, is based on a work by McCullogh and Pitts in 1943, in which it was discovered that the human brain produces signals that are both digital and binary.

 A machine learning system is composed, in principle, by: 1) a problem; 2) a data source; 3) a model; 4) an optimization algorithm; 5) a validation and testing system.

 In 2011, deep learning (DL) added to the other “expert” systems.

It is a way in which machines use algorithms operating at various separate levels, as happens in the real human brain. Hence deep learning is a statistical method to find acceptably stable paradigms in a very large data set, by imitating our brain and its structure in layers, areas and sectors.

As explained above, it is a mechanism that “mimics” the functioning of the human brain, without processing it.

 DL could analyse for the first time non-linear events, such as market volatility, but its real problem was the verification of models: in 2004 Knight Capital lost 440 million US dollars in 45 minutes, because it put into action a DL and financial trading model that had not been tested before.

 In 2013, during a computer block of only 13 minutes, Goldman Sachs flooded the U.S. financial market with purchase requests for 800,000 equities. The same week., again for a computer error, the Chinese Everbright Securities bought 4 billion of various shares on the Shanghai market, but without a precise reason.

 Between 2012 and 2016, the United States invested 18.2 billion US dollars in Artificial Intelligence, while only 2.6 were invested by China and 850 million US dollars by the United Kingdom in the same period.

 The Japanese Government Pension Savings Investment Fund, the world’s largest pension fund manager, thinks it can soon replace “human” managers with advanced Artificial Intelligence systems.

 BlackRock has just organized an AILab.

 In 2017, however, China overtook the United States in terms of AI startups, with 15.2 billion funding.

 China now has 68% of AI startups throughout Asia, raising 1.345 billion US dollars on the markets for their take-off.

China has also overtaken the United States in terms of Artificial Intelligence patents over the last five years.

Nevertheless, considered together, the USA and China still account for over 50% of all AI patents worldwide.

 China also dominates the market of patents on AI technology vision systems, while deep learning data processing systems are now prey to the big global companies in the sector, namely Microsoft, Google and IBM. Similar Chinese networks are rapidly processing their new “intelligent” data collection systems, also favoured by the fact that the Chinese population is about twice as much as the US population and hence the mass of starting data is huge.

 The Chinese intelligence industry zone near Tianjin is already active.

In the end, however, how does Artificial Intelligence change the financial sector?

AI operates above all in the trading of securities and currencies in various fields: algorithmic trading; the composition and optimization of portfolios; validation of investment models; verification of key operations; robo-advising, namely robotic consultancy; the analysis of impact on the markets; the effectiveness of regulations and finally the standard banking evaluations and the analysis of competitors’ trading.

 Algorithmic trading is a real automatic transaction system – a Machine Learning program that learns the structure of transaction data and then tries to predict what will happen.

Nowadays computers already generate 70% of transactions in financial markets, 65% of transactions in futures markets and 52% of transactions in the public debt securities market.

The issue lies in making transactions at the best possible price, with a very low probability of making mistakes and with the possibility of checking different market conditions simultaneously, as well as avoiding psychological errors or personal inclinations.

In particular, algorithmic trading concerns hedge funds operations and the operations of the most important clients of a bank or Fund.

 There are other AI mathematical mechanisms that come into play here.

 There is, in fact, signal processing, which operates by filtering data to eliminate disturbing elements and observe the development trends of a market.

 There is also market sentiment.

The computer is left completely unaware of the operations in progress, until the specific algorithm is put to work – hence the machine immediately perceives the behaviour of supply and demand.

 There is also the news reader, a program that learns to interpret the main social and political phenomena, as well as pattern recognition, an algorithm teaching the machine to learn and react when the markets show characteristics allowing immediate gains.

Another algorithm is available, developed by a private computer company in the USA, which processes millions of “data points” to discover investment models or spontaneous market trends and operates on trillions of financial scenarios, from which it processes the scenarios deemed real.

 Here, in fact, 1,800 days of physical trading are reduced to seven minutes.

However, the algorithms developed from evidence work much better than human operators in predicting the future.

 Artificial Intelligence works as a prediction generator even in the oldest financial market, namely real estate.

 Today, for example, there is an algorithm, developed by a German company, that automatically “extracts” the most important data from the documents usually used to evaluate real estate transactions.

 In Singapore, Artificial Intelligence is used to calculate the value of real estate property, with a mix of algorithms and comparative market analysis. Man is not involved at all.

As to corporate governance, there are AI programs that select executives based on their performance, which is analysed very carefully.

What is certainly at work here is the scientist and naive myth of excluding subjectivity, always seen as negative. The program, however, is extremely analytical and full of variables.

 Artificial Intelligence is also used in the market of loans and mortgages, where algorithms can be processed from an infinity of data concerning clients (age, work, gender, recurrent diseases, lifestyles, etc.) and are linked to operations – always through an algorithm – which are ordered, without knowing it, from one’s own mobile phone or computer.

 So far we have focused on Artificial Intelligence algorithms.

 But there is also quantum computing (QC), which is currently very active already. Its speed cannot be reached by today’s “traditional” computers.

 It is a more suitable technology than the others to solve problems and make financial forecasts, because QC operates with really random variables, while the old algorithms simply simulate random variables.

 Quantum computing can process several procedures simultaneously, and these “coexistence states” are defined as qubits.

In a scenario analysis, QC can evaluate a potentially infinite set of solutions and results that have been randomly generated.

 An extremely powerful machine which, however, cannot determine exactly – as it also happens to slower machines – whether the scenario processed corresponds to human interests (but only to the initial ones known by the machine) or whether the procedure does not change during operations.

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The Reckoning: Debt, Democracy and the Future of American Power- Book Review

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Authors: Junaid R.Soomro and Nadia Shaheen

The chapter is written by Michael Moran in which he discussed about the relations between the economic institutions with the other institutions of the state. A state is a combination of many institutions that work together as a single body to make the state run accordingly. Political and economic institutions are two major components of the state. Politics and economy somehow depend on each other from a very long time. The both concepts are old and influenced by each other. The major changes occurred after the industrial revolution that gave birth to new tactics and opportunities to the economy. Earliest, before the French Revolution the economy was controlled by the elites that were the political identities. This is the example that how those bourgeois controlled the economic structure of the state and how they shape or influence the economical aspect of the society. These involvements of both disciplines gave birth to a new subject that is known as the political economy of the states, that how political and economic policies influence each other because it is not possible for any institution to work separately. The economic institutions shape the economic structure of the state and it is controlled by many aspects, including the political institutions, the economic regulations, the political structure of the state that somehow effects the economic institution of the state.

Summary

The chapter tells us that how economic institution and other institutions are interconnected.

Firstly, the focus is on the political institutions. The recognitions of an economic institution as a political act. The “politics” and “market” are somehow interconnected.   It’s not because the political institutions shape the fate of economy, but the economy shapes it as well. From the start of the history these two aspects are there and depend on each other. We can see it through the examination of the history that how the political elites dominated the society because they were also superior financially. The political institutions somehow legitimize the economic institutions. According  to  “Godin”  different  preoccupations  drive  inquiry  in  different  disciplines:  for instance, choice in economy and the power in the politics.

Secondly the focus is the connection between institutionalism and the economic institutions.

The institutions are constructs of human mind, we cannot see or feel them. The regulations and the market grew up together. The current world politics is an example that how the regulations affect the economy and shape it as different stats can be taken as a model who are following the regulations. The institutions determine the opportunities of the society and in result the organizations are made in order or take benefit of those opportunities. There are several parallels that shape the behavior of the institutions that later affects the other institutions including the economic institution.

Thirdly, the connection between the economic institutions and the regulations.

The regulations are made to control the behavior of the institutions. This faced major change after the industrial revolution when many regulations were made that were supposed to control the outcomes of the institutions. We cannot run from globalization, this is the reason that the concept is not the same as it was in the past, but it came up with the new characteristics. Mainly the  evolution  in  the  middle  of  the  twentieth  century  created  a  paradigmatic  shift  in  the relationship of economic and political institutions. There are agencies with in the states that regulates the working on an institution and on the international level there are multinational corporations. This gives us two basic concepts. The first is uncertainty about the boundaries between the politics and economy, and the second is the importance of the agencies that fills the space and regulates the institutions.

Fourthly, the connection between the economic institutions and the capitalism.

Capitalism  and  the  economy are  directly  connected  with  each  other  because  the  industrial revolution triggered the economy. Industries were made after the revolution and the world faced a new era of progress and economic change.  The modern organizations are the basics that can be taken as the source of understanding the modern political economy. Industries were made after the industrial revolution that mainly works on the productivity, the more the productions are the more it will benefit. This era was a game changer for the economic aspect of the society and later it the economic institutions modified themselves.

Fifthly, the economic institutions and the democratic government.

The  connection  between  democratic  political  institutions  and  the  economic  institution  is complex. It depends that how far democratic government can try to constrain the operations of the economic institutions or how far the economic institutions can try the constrain the operation of the democratic government. the basic aspect of the relation is the relationship between the democracy and the market order. The control of the trade union and the control of the business. There  are  several  problems  such  as  the  tussle  between  the  capitalist  institution  and  the democratic institution. There are several measures that can make both sides work together. The democratic governments usually believe on large economic interests and they also shape it according to their interests. There come the institutional regulations that regulates the behavior of these institutions in the particular manner.

Personal analysis

State is made of many institutions. All the institutions work together this is the reason they depend on each other to work properly. The economic institution is the important institution of the state that makes it stand on its own. Today the examples are in front of us, those states th at has the best economic structures are now ruling the world. USA is the major power but with the passage of time new economic powers are competing with each other. The institutions regulate the behaviors but there are negative aspects when people use the institutions for their benefits. After the industrial revolutions there were merits and demerits. It depends on how one regulates the authority. If the institutions work properly the whole structure can be run perfectly but the interference that affects the institutions negatively can damage the structure. Today in the world where the concept of politics and economy is so dominant it is very important to regulate the bodies properly.

About the Author

Michael E. Moran (born May 1962 in  Kearny, New Jersey) is an American author and analyst of international affairs he is also a digital documentarian who has held senior positions at a host of media, financial services, and consulting organizations. A foreign policy journalist and former partner at the global consultancy Control Risks, he is author of The Reckoning: Debt, Democracy and the Future of American Power, published in 2012 by Palgrave Macmillan. He is co-author of ‘The Fastest Billion: The Story Behind Africa’s Economic Revolution’. Moran served as Editor – in-Chief at the investment bank Renaissance Capital and has been a collaborator of renowned economist Nouriel Roubini as well commentator for  Slate, the BBC and NBC News. He is also an adjunct professor of journalism at  Bard College, a Visiting Fellow in Peace and Security at the Carnegie Corporation of New York, and conceived of and served as executive producer of the award-winning Crisis Guides documentary series for the  Council on Foreign Relations.

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Economy

China Development Bank could be a climate bank

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China Development Bank (CDB) has an opportunity to become the world’s most important climate bank, driving the transition to the low-carbon economy.

CDB supports Chinese investments globally, often in heavily emitting sectors. Some 70% of global CO2 emissions come from the buildings, transport and energy sectors, which are all strongly linked to infrastructure investment. The rules applied by development finance institutions like CBD when making funding decisions on infrastructure projects can therefore set the framework for cutting carbon emissions.

CDB is a major financer of China’s Belt and Road Initiative, the world’s most ambitious infrastructure scheme. It is the biggest policy bank in the world with approximately US$2.3 trillion in assets – more than the $1.5 trillion of all the other development banks combined.

Partly as a consequence of its size, CDB is also the biggest green project financer of the major development banks, deploying US$137.2 billion in climate finance in 2017; almost ten times more than the World Bank.

This huge investment in climate-friendly projects is overshadowed by the bank’s continued investment in coal. In 2016 and 2017, it invested about three times more in coal projects than in clean energy.

The bank’s scale makes its promotion of green projects particularly significant. Moreover, it has committed to align with the Paris Agreement as part of the International Development Finance Club. It is also part of the initiative developing Green Investment Principles along the BRI.

This progress is laudable but CDB must act quickly if it is to meet the Chinese government’s official vision of a sustainable BRI and align itself with the Paris target of limiting global average temperature rise to 2C.

What does best practice look like?

In its latest report, the climate change think-tank E3G has identified several areas where CDB could improve, with transparency high on the list.

The report assesses the alignment of six Asian development finance institutions with the Paris Agreement. Some are shifting away from fossil fuels. The ADB (Asian Development Bank) has excluded development finance for oil exploration and has not financed a coal project since 2013, while the AIIB (Asian Infrastructure Investment Bank) has stated it has no coal projects in its direct finance pipeline. The World Bank has excluded all upstream oil and gas financing.

In contrast, CDB’s policies on financing fossil fuel projects remain opaque. A commitment to end all coal finance would signal the bank is taking steps to align its financing activities with President Xi Jinping’s high-profile pledge that the BRI would be “open, green and clean”, made at the second Belt and Road Forum in Beijing in April 2019.

CDB should also detail how its “green growth” vision will translate into operational decisions. Producing a climate-change strategy would set out how the bank’s sectoral strategies will align with its core value of green growth.

CDB already accounts for emissions from projects financed by green bonds. It should extend this practice to all financing activities. The major development banks have already developed a harmonised approach to account for greenhouse gas emissions, which could be a starting point for CDB.

Lastly, CDB should integrate climate risks into lending activities and country risk analysis.

One of the key functions of development finance institutions is to mobilise private finance. CDB has been successful in this respect, for example providing long-term capital to develop the domestic solar industry. This was one of the main drivers lowering solar costs by 80% between 2009-2015.

However, the extent to which CDB has been successful in mobilising capital outside China has been more limited; in 2017, almost 98% of net loans were on the Chinese mainland. If CDB can repeat its success in mobilising capital into green industries in BRI countries, it will play a key role in driving the zero-carbon and resilient transition.

From our partner chinadialogue.net

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