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Press Release: Recycled Plastic Content Pledges By Coca-Cola And Pepsi Won’t Save The Oceans

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Press Release: Recycled Plastic Content Pledges By Coca-Cola And Pepsi Won’t Save The Oceans

Posted | Updated by Insights team:

Publication | Update:

May 2022
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New research finds leading beverage giants’ recycled content goals won’t address 93% of aquatic plastic bottle pollution. Oceana’s response: it’s time to switch to refillables.

Washington, DC (GLOBE NEWSWIRE) – A new study commissioned by Oceana has exposed the weakness of plastic recycled content pledges by giants like The Coca-Cola Company and PepsiCo. The analysis, conducted by Eunomia Research & Consulting, found that if the top five beverage companies meet their pledges – and it’s far from certain that they will – these pledges would only reduce aquatic pollution from single-use plastic bottles by 7%. In response, Oceana is calling on major beverage companies to adopt or expand strategies that prioritize refillable bottles.

“This report uncovers some worrying realities. It seems improbable that the recycled content pledges by large soft drink companies will be met and regardless, they won’t go far in helping the oceans,” said Dr. Dana Miller, Oceana’s Strategic Initiatives Director. “Adding more recycled content doesn’t stop a single-use plastic bottle from reaching the seas, but replacing that bottle with one that will be reused does. Recycling alone is not the solution that our oceans need. Our oceans need us to return, refill, and reuse our bottles instead.”

The Coca-Cola Company, PepsiCo, Nestlé, Danone, and Keurig Dr Pepper have pledged to increase post-consumer recycled content in their polyethylene terephthalate plastic (PET) bottles by targets ranging mostly from 25 to 50% by 2025. But, according to Eunomia’s analysis, achieving these targets would require collecting an additional 2.57 million tonnes (2.83 million U.S. tons) of plastic bottles for recycling each year. However, there is no coherent strategy in any global region apart from Europe to reliably increase the supply of recycled PET for the production of bottles and achieving this would likely require significant government intervention. Recycled PET sourced from plastic bottles is also high in demand for other uses like making other plastic packaging, clothes, and toys, and this demand is steadily growing.

Of the approximately 511 billion PET bottles used in 2018 in the 93 coastal countries included in the analysis, an estimated 35.8 billion bottles entered aquatic systems. Even if the companies could live up to their pledges, their current commitments would have little impact on reducing aquatic plastic pollution, Eunomia found. This is largely because bottles used for recycling are expected to predominantly be derived from already collected and managed waste streams rather than from mismanaged waste or littering. On this basis, if all brands reached their targets, 33.4 billion bottles (93%) would keep flowing into rivers, lakes, and oceans.

Project Director for Eunomia Chris Sherrington stated that, “Our study found that significantly reducing the flow of used PET bottles to aquatic environments requires collection infrastructure to be introduced in places where none currently exists. While increased demand for recycled content can be expected to lead to a greater focus on obtaining used bottles, it doesn’t necessarily follow that this will all translate into the establishment of new collection infrastructure while opportunities continue to exist to divert already collected bottles from going to landfill or incineration.”

Eunomia’s study also discusses how to increase the beverage sector’s collection rates, including the use of refillable bottles. In 2020, Oceana published a report which found that increasing the market share of refillable bottles by just 10% in all coastal countries in place of single-use PET bottles could reduce PET bottle marine plastic pollution by as much as 22%. If major soft drink companies actually want to reduce the billions of plastic bottles entering the oceans each year, Oceana calls on them to provide a refillable option to consumers worldwide.

Miller added, “Leading soft drink brands need to stop distracting consumers concerned about ocean plastic pollution with pledges about recycled content. The companies responsible for this crisis facing the oceans need to focus more on solutions that can go further in tackling the problem – like refillable bottles.”

Refillables have proven to be very effective at reducing waste. Companies own, track, and collect these bottles, and people who buy refillable bottles typically return them to the place of purchase in exchange for a ‘bottle deposit’. The bottles are then collected, washed, refilled, and delivered back to stores where they can be purchased again. Refillable bottle systems create less plastic waste as each bottle can be used up to 20 times if PET or up to 50 times if glass.

Following campaigning by Oceana and its allies, in February of this year, The Coca-Cola Company announced a major commitment to reach 25% reusable packaging by 2030, leading the way for other companies to follow suit. A month later, PepsiCo announced that they would be making their own time-bound goal on reusable and refillable bottles by the end of 2022. Oceana celebrates these announcements but underlines the importance of these commitments being met and for the companies to embrace refillable bottles as a core business strategy, leading a global revolution back to a former, less wasteful way of living.

Photo courtesy of Marcus Linder.

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This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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