Clean Technology: On Top Down and Bottom Up Assessment of Business Plans

Date:   Tuesday , March 02, 2010

Many observers have identified clean technologies as being among the key drivers of market growth in the coming decade. Diffusion of these technologies is expected to affect the global environment and provide innovation opportunities to individuals, communities, businesses, and entire regions. Examples of such technologies engendering business opportunities range from wind turbines being developed by GE, carbon sequestration capability offered by firms such as InnoSepra, eco-friendly air conditioners, and refrigerators developed by LG, to smart metering devices offered by CISCO. Large investments are being made by governments and by firms such as Khosla Ventures to promote entrepreneurial efforts in this sector. Consequently, we are witnessing a rapid rise in the number of business plans being pitched to investors. A major uncertainty during the development of such plans, whether for a capital intensive and project finance based development, or for software plug-ins for Web-based PowerMeter applications, is the size of the relevant market. Investors are interested in examining both the top down, i.e. starting with a macro view of underlying business trends, and a bottom up, i.e. starting with the consumer preference analysis of such projects.

A question that is commonly raised during business plan competitions in which we have been involved is “Are there unique features within the clean technology sector that affect the sizing of markets and the development of business models?” Another important question is “While these markets appear to be so highly attractive ? and there are incentives being offered by many governments ? why do we not see rapid diffusion of clean technologies?” After reviewing a number of such plans, these five common factors emerge that seem to be creating unique pitfalls and opportunities as they relate to market size projections in this sector:

* Co-diffusion
* Data quality
* Financial incentives
* Geographic differences, and
* Consumer behavior


Usage patterns of conventional technologies, such as a mini-van in the automotive sector, tend to grow in a predictable manner based on the product’s ability to meet consumers’ needs, while also being spurred by advertisements and by word-of-mouth. Other technologies, such as a video game console needs complementary products or services in terms of software applications.

Many clean technologies, on the other hand, rely on the simultaneous growth of the product or service offering (e.g. a hydrogen powered car) and its complementary infrastructure (e.g. a network of hydrogen refueling stations). The timescale for the development of this infrastructure, ranging from 10-30 years, is typically slower than the 3-5 year lifecycle of a product that it complements. This is known as a co-diffusion process, which delays the demand growth for individual products. It affects the top down projections in a negative manner, but does not affect the bottom up projections. Most business plans tend to either ignore or underestimate the delays caused by co-diffusion.

Data Quality

The second pitfall involves either unobserved data or difficulties in measuring the key parameters in a precise manner. For instance, the carbon footprint and allied lifecycle assessment for an infrastructure project within a retail supply chain (e.g. Walmart’s suppliers who build consumer electronics) are difficult to detail. Investors wish to downplay potentially unreliable, and at times unobservable, data and simply ask entrepreneurs to discount these data while estimating the market opportunity. That is, this type of aggregate data with poor quality affect the relevant top down projections in a negative manner, but do not affect the bottom up projections.

Financial Incentives

The impact of financial incentives can be confounding because they affect both the top down and the bottom up projections. For instance, a process technology firm such as the bio-fuel technology firm Verenium Corporation may qualify for federal loan guarantees if it can demonstrate its production capability from a pilot scale (e.g. 1.4 million gallons per year) to a commercial scale (projected at 36 million GPY). Receiving such loan guarantees opens up credit and facilitates access to new markets. However, this type of scale up requires a sizable capital investment upfront, while the operational outcomes are far from certain. Thus, the market sizing for top down assessment is crucially tied to the design of the business model. Many firms use financial incentives to create hedges against uncertainties during such scale up operations. Similarly, the bottom up market sizing is linked to the design of financial incentives through contracts offered to the end customer. In some cases, these incentives are driven by geographic differences.

Geographic Differences

Significant differences can be caused by ready availability of a natural resource, such as sunlight. Other differences can be due to factors such as environmental geo-politics and local, state, or national laws. Within the US the renewable portfolio standards differ across various states. For instance, California has mandated that 33 percent of its power generation be derived from renewable sources by the year 2020. However, it does not specifically mandates minimum solar or customer site requirements. Neighboring Arizona, on the other hand, will require that only 15 percent of its power be generated from renewable sources by the year 2025 and it has additional mandates in place for solar and customer site requirements. Thus, the geographic segmentation of target markets and choice of supply chain partners for a solar technology integrator firm, such as PVT Solar, become crucial assumptions while making top down projections. Geographic effects can be exacerbated when one accounts for global differences. Going back to the example of solar panels, the Indian government has recently mandated the use of solar-powered equipment and applications in all government buildings including hospitals and hotels. The resulting increase in demand could be significant: nearly 1,000 MW of generated power in the next 3 years.

Consumer Behavior

Lastly, an essential factor to consider is consumer behavior. Consumers are influenced by cost, financial incentives, concern about the products, and environmental consciousness. For example, in the US, individual consumers have been offered solar renewable energy credits (SRECs). Institutional consumers or demand aggregators are also able to take advantage of this opportunity by trading these credits. Similarly, demand response, i.e. downward adjustments in usage during peak demand, is a valuable opportunity for individual consumers as well as business-to-business (B2B) aggregators. However, large-scale diffusion of such technology within a consumer segment assumes that those consumers will modify their behavior and operating procedures, e.g. turn down air conditioning even by a small fraction, at the peak load hours. Assessment of the resulting market size becomes complicated because of linkages between demand, capacity, and market clearing prices. One of the best methods to understand the consumer reaction to such issues is through market research using focus groups or through more involved behavioral studies.

Simplifying Complexity

One goal for identifying these factors is to inform the thinking of business plan developers. At large firms that are endowed with planning resources, a growing trend is toward scenario thinking in an effort to juxtapose the effects of several factors and thereby simplify the complexity in the business planning processes. A best practice we have observed among small teams and entrepreneurs is to focus on understanding consumer behavior and to develop niche segments and supply chain strategies.

The five factors discussed above shape the top down and bottom up views of market assessment quite differently. However investors look for analysis, which shows some consistency across these two views. These investors often see business pitches that are overly optimistic and counter to their own intuitive understanding of the growth trends in the clean technology sector. Simplifying complexity and careful accounting of these factors ought to build trust and lead to a better assessment of demand growth. Understanding of the growth challenges in the clean technology sector, in turn, can lead to more innovative realization of business models.

The authors are Nitin Joglekar, Associate Professor and Jonathan Hibbard, Assistant Professor, Boston University School of Management. They can be reached at and