Developing Alternative Economic Models: Production, Wealth and Utility Beyond Growth

Most mainstream economic models treat the environment as an afterthought – a resource pool that feeds production and absorbs waste without limits. But what happens when those limits become impossible to ignore? As resource depletion accelerates and carbon emissions pile up, the standard growth models that guide economic policy are proving dangerously inadequate. To build economies that can actually last, we need models that account for what nature provides, what it can absorb, and what happens when we push too far.

Why standard growth models fall short on sustainability 🔗

Traditional economic growth models, rooted in neoclassical economics, treat the economy as a closed system. Inputs like labour, capital, and technology drive output, and GDP growth is the primary indicator of success. Natural resources and ecosystem services barely figure in these models – or if they do, they’re treated as perfectly interchangeable with human-made capital.

This is the core problem. To evaluate sustainability and develop viable policies, growth models need to explicitly account for the relationship between human-made capital and natural capital. They must address a set of interconnected questions: How does resource depletion affect long-run growth? Can technology truly substitute for dwindling natural resources? What are the real limits of the environment’s capacity to process waste? And what role does human capital play in navigating these constraints?

Without honest answers to these questions, any model’s projections about future prosperity are built on shaky assumptions.

Resource depletion and its hidden costs to growth 🔗

The debate around resource depletion often centres on whether substitutes can be found. From a microeconomic perspective, substitution seems feasible – when one resource becomes scarce, prices rise, and alternatives emerge. But this optimistic view misses critical macroeconomic and global-scale effects.

As high-quality, easily accessible deposits of minerals, fossil fuels, and other resources are exhausted, extraction costs rise sharply. More capital, energy, and labour are required to obtain the same quantity of a resource from deeper, more remote, or lower-grade sources. This escalating input requirement is a drag on the economy that standard models largely overlook.

Consider it this way: if an economy must divert an ever-growing share of its capital and energy just to maintain the same flow of raw materials, less remains available for other productive activities. This is not a hypothetical concern – it is a documented pattern in mining, oil extraction, and fisheries worldwide.

Modeling resource extraction as a separate sector 🔗

To capture these dynamics accurately, revised economic models should treat resource extraction as a distinct sector, separate from the rest of the economy. This allows the model to incorporate feedback loops – as depletion progresses, the required inputs for extraction increase, which in turn affects the resources available for consumption, investment, and everything else.

Technology is often cited as the great equaliser here, capable of offsetting depletion through efficiency gains. And historically, technological innovation has indeed reduced the capital needed per unit of resource extracted. But these improvements show diminishing returns. Early gains from better drilling techniques or more efficient engines were substantial, but each subsequent improvement tends to be smaller and harder to achieve. Models that assume technology will perpetually outpace depletion are making a bet that historical evidence does not fully support.

Environmental waste processing: the limits we keep ignoring 🔗

The economy doesn’t just consume resources – it generates waste. And the environment’s capacity to absorb and process that waste is finite. Current economic models handle this poorly.

The most well-known attempt to quantify the economic impact of environmental damage is the damage function used in integrated assessment models (IAMs). The DICE model, developed by Nobel laureate William Nordhaus, is perhaps the most widely referenced. In its standard formulation, the DICE model uses a quadratic damage function that estimates climate-related GDP losses at roughly 2.1% of global income for a 3°C temperature rise.

Why damage functions are deeply problematic 🔗

These numbers sound precise, but they rest on highly arbitrary assumptions. The damage function in DICE does not emerge from detailed scientific modelling of how ecosystems respond to warming. Instead, it is calibrated from a patchwork of sectoral studies, expert judgement, and extrapolation. As economists at the Grantham Research Institute have noted, the model’s own documentation acknowledges omitting key factors like biodiversity loss, ocean acidification, and catastrophic tipping points.

The quadratic shape of the function is itself a critical limitation. It assumes that damages increase smoothly and predictably with temperature. But real-world environmental systems don’t behave this way. Critics like Steve Keen have pointed out that this structure cannot capture sudden disruptions – ice sheet collapses, abrupt shifts in ocean circulation, or agricultural failures that cascade across regions. The model essentially smooths away the most dangerous possibilities.

More fundamentally, these models fail to represent how energy and materials flow between the economy and the environment. Wastes don’t just reduce GDP in the current period – they accumulate, and their effects often emerge with significant time delays. Greenhouse gases emitted today will warm the planet for centuries. Plastics dumped in oceans disrupt marine ecosystems over decades. A model that captures only a snapshot of annual damage-to-GDP ratios misses this entire dynamic.

Can human-made capital substitute for nature? 🔗

The idea that economic growth can continue indefinitely rests heavily on one assumption: that technological progress and human-made capital can substitute for declining natural capital. This is the dividing line between what ecological economists call weak sustainability and strong sustainability.

Weak sustainability, the position of most mainstream economists, holds that total capital – natural plus human-made – just needs to stay constant or grow. If we deplete a forest but invest the proceeds in factories or education, we’re no worse off. Strong sustainability, the position advanced by ecological economists like Herman Daly, argues that certain natural capital is fundamentally irreplaceable and that the economy is a subsystem of the biosphere, not the other way around.

What’s behind observed efficiency gains? 🔗

When we look at the data on energy and material intensity – the amount of energy or raw material used per unit of GDP – many economies have indeed improved over time. But a closer examination reveals that these improvements are not purely the result of technological progress substituting for natural resources. Several other factors are at work.

First, much of the improvement in rich countries has come from switching between fuel types. Moving from coal to natural gas, for example, delivers more economic output per unit of carbon emitted, but it’s still fossil fuel dependence – just a higher-quality version. Second, wealthy countries have often outsourced resource-intensive production to developing nations through international trade. The carbon and materials intensity of their domestic GDP falls, but the global footprint doesn’t shrink – it just shifts geographically.

Models that attribute these improvements solely to technology or capital substitution are misdiagnosing what actually happened. This matters enormously for projections: if past efficiency gains came partly from one-time shifts rather than ongoing innovation, we cannot simply extrapolate them into the future.

The fossil fuel challenge: replacing concentrated energy 🔗

Humanity’s economic expansion over the past two centuries has been powered overwhelmingly by fossil fuels – coal, oil, and natural gas. These fuels are extraordinarily energy-dense. A small volume of oil packs an enormous amount of usable energy, making it ideal for powering industry, transport, and virtually every aspect of modern life.

The challenge of transitioning away from fossil fuels is twofold. Replacement energy sources must: (1) eliminate or drastically reduce carbon emissions, and (2) approach the economic wealth generation per unit of energy that fossil fuels have provided.

Where solar and wind stand 🔗

Renewable energy technologies like solar photovoltaics and wind turbines clearly meet the first criterion. They produce electricity with minimal greenhouse gas emissions during operation. And their costs have fallen dramatically – IRENA data show that the cost of utility-scale solar PV dropped by 85% between 2010 and 2020, with onshore wind falling by 56% over the same period.

The second criterion is where things get more complicated. Solar and wind energy are diffuse – they’re spread over large areas, unlike the concentrated chemical energy in a barrel of oil. Collecting this energy requires substantial material and energy investment upfront: panels, turbines, batteries, transmission infrastructure, and the mining and manufacturing that produces all of it.

A key metric here is the energy return on investment (EROI) – how much energy you get back for each unit of energy invested in building and running an energy system. Research published in Nature Energy found that when measured at the useful energy stage (accounting for final-to-useful conversion efficiencies), fossil fuels’ EROI is around 3.5:1 – considerably lower than the commonly cited final-stage figure of about 8.5:1. The same study found that renewable electricity systems already exceed the EROI threshold needed to deliver equivalent useful energy, even when accounting for intermittency.

This is encouraging, but the transition period itself poses challenges. Building out an entirely new energy infrastructure requires enormous upfront energy and capital investment. During this phase, net energy available to the rest of the economy may temporarily dip. According to research in Nature Communications, all major energy transition scenarios maintain system-wide EROI above the critical threshold, though the speed of transition significantly affects how steeply EROI declines during the build-out phase.

Rethinking wealth, utility, and what growth really means 🔗

The limitations of current models point toward a deeper question: what are we even measuring when we talk about economic success?

GDP captures the monetary value of goods and services produced. It does not distinguish between activity that builds long-term well-being and activity that destroys it. Spending on disaster recovery boosts GDP. So does extracting a non-renewable resource at an unsustainable rate. Meanwhile, ecosystem services – pollination, water purification, climate regulation – that underpin all economic activity go largely uncounted.

Ecological economists at Boston University have argued that beyond a certain level of consumption, the marginal benefits of growth are outweighed by increasing environmental and social costs. When economic production crosses ecological thresholds, it becomes what Herman Daly termed uneconomic growth – growth where the extra costs exceed the extra benefits.

Alternative frameworks gaining ground 🔗

Several alternative frameworks attempt to address these blind spots. Doughnut Economics, developed by Kate Raworth, proposes that economies should operate within a space bounded by a social foundation (ensuring everyone’s basic needs are met) and an ecological ceiling (preventing overshooting planetary boundaries). The goal is not endless growth but thriving within safe limits.

Steady-state economics, rooted in Daly’s work, distinguishes between growth (quantitative increase in throughput) and development (qualitative improvement). It argues that sustainable development means improving technology, design, and social institutions without increasing the physical scale of the economy.

Well-being economics shifts the focus from GDP to broader indicators of societal health – life expectancy, educational attainment, environmental quality, social equity, and subjective satisfaction. Countries like New Zealand and Scotland have already begun incorporating well-being measures into policy planning.

These are not fringe ideas. They represent serious attempts to build economic models that respect the physical and ecological constraints within which all economies must operate.

What revised models must actually do 🔗

For economic models to be useful guides for sustainability policy, they need several upgrades over existing frameworks:

Separate resource extraction from other economic activity. This allows models to capture the rising costs of depletion and the feedback effects on the broader economy.

Represent waste accumulation and delayed feedback. Environmental damage isn’t a simple percentage haircut on annual GDP. It involves stocks that build up and thresholds that, once crossed, trigger non-linear consequences.

Be honest about substitution limits. Not all natural capital is replaceable. Models must distinguish between cases where technology can substitute and cases where it cannot.

Account for the full cost of energy transitions. The shift from fossil fuels to renewables involves massive upfront investment. Models must capture the temporary energy and capital costs of this transition, not just the long-run equilibrium.

Redefine the objective function. If the goal is maximising GDP growth, models will always recommend more extraction and more consumption. If the goal is long-term human well-being within planetary boundaries, the policy recommendations change fundamentally.

Moving forward with clear eyes 🔗

The urgency of climate change and resource depletion means we cannot afford models that offer false reassurance. The DICE model’s suggestion that 3°C of warming would cost only a few percentage points of GDP has been widely challenged as dangerously complacent. Real-world impacts – displacement, conflict, agricultural collapse, biodiversity loss – don’t fit neatly into a quadratic equation.

At the same time, the energy transition is not a dead end. Renewable energy technologies are improving rapidly, and recent research suggests they can deliver net useful energy comparable to or exceeding fossil fuels. The challenge lies in managing the transition – financing the upfront costs, building new infrastructure, and ensuring that the benefits are equitably distributed.

Alternative economic models – from steady-state economics to doughnut economics to well-being frameworks – offer conceptual tools for navigating this transition. They won’t replace quantitative modelling, but they provide the philosophical grounding that current models lack: an acknowledgement that the economy exists within the biosphere, not above it.

What do you think? If GDP growth is no longer the right measure of economic success, what should replace it – and who gets to decide? Can economic models ever fully capture the complexity of ecological systems, or should policy rely more on precautionary principles than on optimisation?

References

1. https://en.wikipedia.org/wiki/Ecological_economics
2. https://en.wikipedia.org/wiki/DICE_model
3. https://www.lse.ac.uk/granthaminstitute/news/a-nobel-prize-for-the-creator-of-an-economic-model-that-underestimates-the-risks-of-climate-change/
4. https://www.exploring-economics.org/en/discover/climate-economics-and-the-dice-model/
5. https://www.localfutures.org/ecological-economics/
6. https://www.irena.org/Digital-Report/World-Energy-Transitions-Outlook-2022
7. https://www.nature.com/articles/s41560-024-01518-6
8. https://www.nature.com/articles/s41467-023-44232-9
9. https://www.bu.edu/eci/files/2020/01/Alternatives-to-Growth_final.pdf
10. https://openoregon.pressbooks.pub/socialchange/chapter/5-6-new-economic-models/