Evidence base: how better data strengthens waste services

This transcript has been lightly edited for clarity. Names and technical terms have been corrected, and brief webinar administration and poll mechanics have been removed. The substance of the conversation has not been changed.

Zoë Lenkiewicz: Thank you, Swetha, and welcome everyone. Very excited to be here again. Last week, Mayor Talib Bensouda opened the Built to Last series by showcasing what leadership and governance can really achieve. Under his leadership, Kanifing Municipal Council increased waste collection from 4% to 76%, created jobs, strengthened its systems and improved the management at Bakoteh dumpsite. He described waste management and economic development as a two-way relationship. Okay, so growing economies generate more waste and need better services, while clean, green cities protect people, attract investment and enable further prosperity. Kanifing's progress is striking and the figures help us understand its scale. But evidence is not only something we collect afterwards to demonstrate success. It should help us understand the problem, decide what to do, avoid expensive mistakes and improve services as they develop. The Built to Last series explores Global Waste Lab's seven foundations of a resilient waste project, developed through years of field experience and research for the United Nations Global Waste Management Outlook 2024.

The series runs up to the International Solid Waste Association's World Congress in London this November, my home city, very excited, where Global Waste Lab is a knowledge partner, and the Chartered Institution of Waste Management has endorsed this series for continuing professional development. So our wheel is back, as you can see. The foundations are interconnected, and as I said in session one, we need waste systems to keep on rolling, which is one of the reasons it's a nice wheel. Today we focus on the second foundation, a strong evidence base. For this session, I've grouped it into three practical elements. Baseline understanding means knowing what waste is generated, where, whether it's by households or businesses, for example, what happens to it now, and what local conditions can help shape the system. Informed decisions mean using that knowledge to choose services, routes, vehicles, facilities, technologies and budgets that fit the real context.

And monitoring and adaptation mean using operational data and community feedback to see what is working, identify problems and improve over time. A strong evidence base does not mean waiting for perfect data. In many places, reliable information is scarce. Research budgets are small, if not tiny, and conditions change quickly. Estimates and assumptions are sometimes unavoidable. The challenge is to be honest about uncertainty, test the assumptions that could make or break a decision and keep learning. Right, now then, I'm going to be introducing Pushkar. I first met Pushkar at the ISWA World Congress in Kuala Lumpur in 2018, and I'm very happy to have him with us today. Pushkar is CEO and co-founder of EcoSense Enviro Solutions. His work includes baseline studies and city waste modelling with GIS, GPS tracking, performance monitoring and citizen feedback. In other words, he looks at evidence not as a report that sits on a shelf, but as something that should shape planning and everyday delivery.

Last year I ran a LinkedIn mini-series on the Seven Foundations that we're talking about here. You can find the discussions in my featured posts on the subject of the evidence base, Pushkar made the point that solutions rooted in local context are going to be more practical, more cost-effective, and more sustainable. And that's exactly the perspective that we are here to explore today. So Pushkar, thank you so much for joining us and bringing the second foundation to life. Over to you.

Pushkar Pradhan: Thank you so much for having me here, and I think this particular topic is quite a vast topic. We are going to try to cover it in a short span of time so that we can get some more Q&A going. But the idea is to be able to pass on the understanding of why an evidence base is so important. When we take up any of these projects in waste management, as we all know, we are, you know, working with very limited resources because we have very little money to spend on waste management. So it's more necessary that we use it pragmatically and deploy the funds such that, you know we can get the best out of it. I'll start off with this particular slide here, because I think this is important to understand that currently we are generating 2.56 billion tonnes of waste. And this is projected to increase to 3.86 billion by 2050. That's a substantial increase that we'll be seeing.

In fact, the types of waste will also change quite a lot, as we progress and as the societies change. This also means that we. when you look at any of the reports currently. There are a lot of countries and cities that are not receiving adequate waste management services. Yeah, so they are not getting the services for collection of the waste, processing it, and hence they are often ending up in what may be called a landfill but is actually a dumpsite and Alongside these challenges, there is a huge potential in terms of how we can deploy funds and how can we save money here. So with that particular context, I'm going to be moving to the next slide, which is where we will be talking about how we build an evidence-based approach rather than relying only on assumptions, yeah? So right now, many of the gaps we see arise from reliance on assumptions.

And whenever we come up with assumptions, that's where we remain on the back foot. So on the left-hand side, the general waste gaps that we have observed so far, yeah, and these examples are only representative and do not cover everything. One example is that the generation rates are borrowed from other cities. Yeah, there is not enough time spent or efforts involved for measuring local conditions. And against that, when you look at an evidence-based approach, it starts with actually mapping out the waste flows. And when you look at waste flows, they have to be understood in the local context, because every city has a different way in terms of how waste is generated, in terms of how they take care of the waste, and where it eventually ends up. Plus, of course, the climatic and geographic conditions matter a lot, and which is why rooting the evidence in local context is essential. The second area is health benefits.

When we claim something that, hey, we have done this, and an intervention has reduced a particular disease, we often lack a baseline showing the original figures, and after making interventions, it has improved in a certain way. So against that, when we look at health and environmental baselines, it is not always possible to establish direct causation, but we can establish a baseline and monitor change. In fact, in the last webinar, we heard that when they did those interventions of drain cleaning and ensuring that waste collection is happening on a timely basis, they could see the number of malaria cases reported by hospitals had gone down. So that's a direct impact that comes out of it. Similarly, Economic analysis must also examine local financial realities and market conditions. This links to the point about local offtakers, because when we take an evidence-based approach, we need to look at an end-to-end solution.

Essentially, when we look at assumption-based approach, where decisions are made in silos, that's where efforts to create a complete waste cycle can be lost. So here we are looking at how can we understand that when I collect this waste and I process it or segregate it, who are my offtakers? What are they going to be doing with that? How do I ensure that circularity is built into its management. Similarly, another major consideration is geography and culture. We also need to understand how people currently are managing their waste. What is their culture? Because sometimes, you know, you want to. and this is typically what we have observed a lot that we have a lot of solutions from Western countries imposed on developing countries, right? And that is often where they fail. And there are certain cases, like, we will come up with some examples of waste-to-energy plants failing. For me, the most important considerations are the local offtakers, and ensuring sustainable finance.

An evidence-based approach can help ensure that we are able to tap into sustainable financing mechanisms to make the system financially sustainable, rather than just relying on grants or CSR support coming in. So that's the main thing. To return to Zoë's earlier point, that when you look at any strong evidence base, it's not only about perfect data, but it is about asking how best to use the available information and how can we test assumptions so that we can learn and adapt from it? Yeah, and it's a continuous thing, so it should not exist only on paper, but should help us make better decisions. With that, I'll come to this main slide, which is on the city planning flow. The first layer is talking about collection of the data. The second layer talks about integrating that into certain models, and the final layer is about outcomes that you can expect because of data that is collected earlier. So when you look at city population data.

You need to look at growth projections of at least over five to 20 years, so that we avoid introducing a knee-jerk solution. Income distribution matters a lot because the type of waste and the waste generation rates also increase substantially. We need a breakdown by area so that we can plan at a block level, rather than, you know, planning at a city level because eventually you will have to break it down from the city to zones and wards, and within the wards multiple blocks, right? So if you can break it down, that is the best way of making it localised. The second part of it is looking at characterisation.

This is essential because when you look at characterisation, you will be looking at what type of waste is coming out, where is it coming out from, what is my per capita generation of waste and why this is important is that then you can model it to identify that if my population increases, then this is the potential increase in waste generation that we can expect. This helps determine the type of processing options that I need to adopt in practice. For example,, depending on whether you are considering a waste-to-energy plant, then you need to look at the composition to identify that there is enough calorific value available for making it self-sustaining for combustion. Similarly, for seasonal variation, you need to examine at least two seasons, that's the recommendation, so that you can identify how waste characteristics change, and how it's going to be managed going forward. Similarly, going forward on that is the third part on GIS and topography. Where is the existing infrastructure?

Where are my transfer stations going to be set up? Because based on the tonnage and the terrain, you need to identify where your transfer stations are going to be so that you can identify and plan infrastructure more effectively, including landfills, right? So, imagine this, that a municipality may allocate a landfill, but no one. if they have not done any study on the topography of that landfill, it is difficult to estimate how quickly it will fill. Let's say 100 tonnes of waste every month, how much time is it going to take for that landfill to be filled up? Alright, so that's a simple calculation that can be brought in when you look at GIS and topography planning involved in that. And local, local, local. I cannot emphasise that enough, but, you need to be able to get people on board.

Actually, because they are the ones who are going to be the generators as well as managers of the waste, including workers in an informal economy, whose knowledge and activities should be recognised and integrated. Consider the local geography, climate, culture and economic context. So all of these things are something that you'll need to be taking into account. Whatever your role, including as a consultant, try to see if you are able to find local partners, because they will be able to give you a better context of what is happening, and that is where the strategy and approach can become much more effective. I'm going to come to the integration layer, which is where we do the city waste modelling. So here is where we could look at, you know, growth projections, financial viability modelling, landfill projections based on the GIS study done earlier, including, let's say, resource planning.

Infrastructure planning, resource planning is saying the number and capacity of vehicles needed based on the capacity, the routes they can cover and the transfer stations required, where are my, you know, connections needed across the transport network. The third one is on the circularity part. My apologies if I am going a little fast. I just want to ensure that we get the context of what is there, and then we come back to the Q&A part. So that's where we would like to spend a bit more time so that it becomes more interactive. And we can learn from each other. So, Processing and circularity are other areas to examine, again identifying local offtakers.

And finally, the landfill closure and planning, which relates to the point that I was mentioning earlier In terms of saying that, okay, if a certain quantity of waste is dumped, it will take 4 years for a landfill to be filled up, but if I'm able to divert, let's say, 40% of the waste away from landfill, it increases my landfill life by another 7 years. So it goes to 11 years, right? So those are the types of decisions that can be brought in. Finally, we consider the outcomes. So, operations monitoring brings in efficiency in terms of how do you deploy your resources. Cost optimisation, which is one thing I will talk about in my next slide. Building a circular economy framework means connecting offtakers and other actors across the system.

Sustainable financing can include, which is your carbon credits, plastic credits, EPRs, green credits, and other mechanisms and All of this data can also help in terms of setting up policy and governance protocols The main thing to understand here is that it's not a one-time activity, because data, waste characteristics and local conditions change. So you'll have to keep on recalibrating it periodically. So this is what we do essentially in terms of just some examples of what we have been doing at EcoSense as well, where we do infrastructure mapping, which we use to plan routes, etc. Looking at KPI-based monitoring systems that you can develop so that you take out the subjectivity and make it more objective on what is happening in practice. And then coming up with some sort of frameworks here to identify at the ward level. How can I come up with assessment frameworks for different cases?

Like, for example, this particular framework is about identifying segregation at source at ward level or block level and whether it is working well or not. So you can then come up with more of those information, education and communication activities which are contextually based. This was the example I was trying to mention earlier when we looked at evidence base. So this is a real example where, you know, before we actually looked at data and identified, okay, where is my vehicle going around and the amount of waste collected, it was earlier running around 82 kilometres per day Using optimisation models, we reduced this to 55 kilometres per day, If you examine the figures, and of course with the increase in the fuel cost now, it actually is a substantial gain without having to do over-engineering in there. So beyond the cost part of it, I think I would also mention quickly about the sustainable financing part, which is very, very important.

You have health and environmental benefits that can be there. You have social and governance benefits, which is something driven based on KPIs, and it makes it much more transparent, and hence there is a lot of public trust that can be gained. And, essentially, investability, right? So we all need funds coming in, and funding is more likely when you can provide stronger evidence. Similarly, and I think I just wanted to put this slide here because beyond municipal waste, there are also many developments that happen with EPR, with plastic credits, carbon credits. And this was something which we talked about product passports. I just put in a very rough one here, but when you look at products that are manufactured from the manufacturer to the distributor, to the consumer and eventually discarded at end of life, the entire cycle can be digitised, and this is where some of the AI-based solutions on waste identification can also be plugged in.

And this can help identify and access different forms of credit that can be there, right? So it brings in that sort of accountability. You can use that data in multiple ways. I'm not going to spend time on that right now but yes, there are multiple avenues and modes that you can look at. I wanted to leave you guys with some practical resources that you could tap in. And this is based on information that is available as open-source material, but something I just wanted to bring. One thing is on frameworks. So on the left hand side column, you'll find what are the steps that can be done when you look at any sort of evidence-based approach, starting with baseline audits, GIS, topography and modelling, digital operations layer and Always involve citizens, because they are the main generators as well as the stakeholders. And the tools, resources that you can tap in here.

And a lot of these tools are very, very important and essentially something that are like a guiding framework such as the Waste Wise Cities Tool from UN-Habitat and I believe a lot of us might already be using that. And some of the tips that you can tap in here, combining studies with GIS information and landfill timeline mapping and monitoring part of it and making the project finance-ready, An evidence-based approach can also help you roll a project out faster. And, whether you're a municipality, or a funder, or an NGO, or a consultant, I think the approaches might be a bit different, but all of us are working towards more efficient waste management, which is where. You could look at multiple types of data that can be tapped in. And I just would leave you with some examples of waste-to-energy plants, right?

We have seen many of these waste-to-energy plants failing because the waste-to-energy plant was developed without a characterisation study before it was established and then you have a lot of this mixed waste with a high organic fraction, which is almost like around 65% organic material and only 35% dry material in many developing countries and that's where, you know, the waste may not have sufficient calorific value, and hence it fails. Some slides here. This is again something which might be helpful for you to, like, keep in mind. Like, when we do any sort of this, of course, the cost matters, the timeline matters, and what is the methodology that you can adopt. So here, I've tried to put in something where. It considers the activity and who you can engage to make it happen.

Approximate cost, I think it's just something which is a, you know, a guiding one, it's not exact, so things can change based on local conditions, timelines that you can look at What I wanted to leave you that, with is that if you look at this entire cost of doing any sort of evidence-based planning and looking at, let's say, a city of half a million people, this might cost roughly 80,000, 90,000 or 100,000 for example, yeah By comparison, a single refuse compactor truck could cost as much. So it's at a fraction of the cost. If you just put in those methodologies and steps in place, you can make things happen better from a long-term project perspective These steps also form part of the Waste Wise Cities Tool. So you can go through this and you'll leverage and use this methodology to be able to do your evidence-based planning at the local level. And these are the references. So I'll leave you with these slides.

I'll stop my sharing. And now I think, Zoë, I would be happy to pick up any questions that come up.

Zoë Lenkiewicz: Thank you so much, Pushkar, for a really thorough and fascinating presentation. And before anyone asks, yes, I do believe Pushkar is very happy to share his slides with you all afterwards. So as long as you're signed up to the Global Waste Lab newsletter, you'll be receiving that from Swetha. So, let's start with this fragmented data and so on. I'm going to start with my first question to Pushkar, which is that if a city or a council, because I know many people here don't work in big cities, they're often maybe secondary cities or towns or even villages. If they can only collect a small amount of data before investing in a new system, what do they absolutely need to know? What's the minimum useful evidence base?

Pushkar Pradhan: I think one of the key thing is that for any city, right, they already have the data available with them. They're collecting the property taxes. So if you're able to ask them that, hey, in my city, how many residential and commercial properties there are and their scale. And let's say for residential, if you are able to just put in some rules saying that based on the study for that local area, let's say it's 450 grams per person per day, right? You assume a household with three people, four people, using reasonable assumptions and from there, you can build up data to identify the amount of waste likely to be generated, right? And that data point can help you to identify. What is my infrastructure requirement going to be without complex analysis or extensive fieldwork. So those are a few things that you can start with essentially before going to anyone.

One of the key things that I have observed is that because the evidence base is something which is often considered too late, you know, the key is helping people see its value earlier. And if you're able to make that calculation and go to them and present it to them, I think you have someone who can be a champion for you within that municipality.

Zoë Lenkiewicz: Yeah, I like that point, Pushkar. Thank you. And one of the things that I really picked up on from your slides was that data doesn't just help with operations, but it can also help with building trust. As we discussed in last week's webinar with Mayor Bensouda, trust and behaviour change and service performance kind of they're all, you know, they all reinforce one another, don't they? You know, you can't get people to change their behaviour until they can see that there is a reliable collection service that is going to turn up every week Or that if we're asking people to separate their waste, they need to see that it's not all being put back together again in one mixed truck. You know, sometimes, I don't know, I see a lot in Europe, the collection trucks, they do have separate compartments inside them and I feel like it's a massive missed communications opportunity because people don't see that.

They just see the bins being all emptied into the one truck and they think it's all getting mixed in together. So then trust is lost and people think, well, why should I bother? And that then affects operations, which affects the finance and makes the whole thing less affordable. So it's, you know, all of this does really tie together, and I like how you know, you tied in your points with these other, foundations from the Resilient Waste System, seven foundations, because they are all very interrelated. So On that, okay, so we've got the quantitative data that we can collect on, you know tonnes and composition and the location of densely populated low income areas, less densely populated middle income areas, and the different waste generation that we would expect from those places. What I would like to find out from you is, how do you combine technical data, this quantitative data, with what communities and workers already know?

Yeah, how do we make these, the kind of the data gathering more inclusive so that, as consultants or advisors or as council officers, we're not missing a big bit of information that could really help to inform our system.

Pushkar Pradhan: That's a very interesting question because I think it also brings in that part of inclusivity, right? So getting the citizens to be more engaged with their waste and taking responsibility for it. Because right now it is like out of sight, out of mind, right? I just throw it away and it's all gone Especially happens with places where you have the apartments, and you have a chute system, right? So you open the chute and just throw it down, so you just don't see what is happening there with that waste. One thing could be, like, of course, the behavioural changes have to be, you know, like, it has to be an iterative process, and you require continuing attention and adjustment. But my observation has been that if you are able to come up with clear information that you can give them, saying that, hey, you know, from this region. This was from India, this was the waste generated and the baseline, etc.

And let's say the segregation levels was a bit lower. You could also come up with some healthy competition between two different blocks in that one to say that this block is doing good or not. So it's just something which keeps them engaged and eventually, you know, you see that change coming in quite a lot, because people want to feel included in there. You could tell them that you could run information and communication campaigns through any of these digitization tools to say that, hey, this is what has been happening linking it up with generative AI is not very difficult. They can just look up and say, how do I do my compost at home, etc. So those things definitely help a lot.

Zoë Lenkiewicz: Yeah, that's great. And Hamza is here in the Q&A. Thanks for your questions, Hamza. Asking if you've seen any data that's been consistently built around behavioural change towards waste segregation. So any insights that you've got there.

Pushkar Pradhan: I think that's one of the most neglected areas, to be very honest, because while we are looking at the bigger picture of doing a lot of things, I think behavioural change is very, very difficult. It is difficult to embed, and there are not many consistent data points. So, if you're able to do that at a certain level and then scale it up to a ward level or zone level, that will be more helpful. But my observation has been that it has been very, very localised and something which, for some reason has not been able to scale up, because it needs a lot of those sort of, you know, the impact happening all across the place. I am happy to hear if anyone has any, Zoë, if you have any different observations on that front.

Zoë Lenkiewicz: I completely agree. It takes a long time. So when I first started working in waste management in 2002, the council that I was working for, we were rolling out the first ever recycling collection programme. So we were collecting the residual waste and the recycling. And it's taken, you know, a long time to get up to a good level of participation and capture rate. So, by participation, it's how many houses in the street, for example, are actively separating their recyclables and then capture rate is, you know, if, say I'm a householder, am I putting all, am I separating all of my recyclables or just some of them? So you've got two targets there with behaviour change to achieve, which is, it's not easy, and it does take time, and I think that one of the biggest lessons that. that I've seen councils learning is that People do not want to feel left out. Yeah, people want to feel that they're in the majority.

So if only one or two households in the street are clearly separating their waste, then there's no pressure on me to do so. But if 80% of the households are doing it, then I start to think, oh, hang on, I don't want to be the outlier. I don't want to be the, you know, the only one that's not doing it. So I better change my behaviour to join in with the crowd. And that's, you know, we see that and that's quite consistent across different income levels and different cultures as well. I think it's very much human nature and definitely something that we can tap into with our communications then to get people to participate in the way that we need them to.

Pushkar Pradhan: Absolutely, and if I can just share one example of. so the recent 2026 waste management rules published by the Government of India, there are a lot of changes that are happening with the bulk waste generators being asked to take up ownership of the waste and the government is digitising it at the central level. So imagine the country as the size of India, and then all of the data is going to be centralised at some point, where they're asking the BWGs to be registered, and being able to push their data to the portal regularly. Second example of that is, when we were looking at two-way segregation, four-way segregation, one very good example was in Panaji, in Goa, where they are doing 16-way segregation at the society level. I was, like, amazed with the way that it was being done.

But residential communities participated, because they were able to see value in that since they are able to also, you know, sell their waste and get some revenue out of it. But the corporation or the councils are also being very supportive of that, and they are looking at how can we give them property tax rebates. So it's a win-win situation for everyone in that case.

Zoë Lenkiewicz: Nice, collecting the data and then using that data to further incentivise communities. Yeah, love it, love it. Okay, awesome. Right, going back to the poll questions. Many people in this webinar said that one of the biggest challenges that they're facing is fragmented data, weak sharing, or unclear ownership. Can you give us an example of where you might have seen that, and how that challenge was overcome.

Pushkar Pradhan: Hmm, I think it's a practical challenge when you actually start trying to get data, people are first and foremost very apprehensive because they feel that you're going to misuse the data. Simple example, like when you are going to a landfill. They do not give you that data because they feel that, you know, people can trace it back to say that, okay, how much is coming? And there are challenges and loopholes in the entire ecosystem over there. So from that perspective, I guess what can happen is that We will have to look at where some of the data points are collected, including suppliers and relevant third parties. So when you look at data collection, first and foremost, identify the main actors from whom the data can come. How can I get it, whether it can be obtained directly or estimated through a reasonable assumption?

So wherever you can actually make a sound, evidence-based assumption, I think go with that, instead of breaking your head over not being able to get any information, because rather than having no information, you can at least make some much more, you know, like, structured, evidence-based estimates around it. The second part is then look at stakeholders and see, get them to understand the bigger picture of what you're trying to do. But, yeah, I guess it's something which is in the works, right? So you have to keep working at it to get that data point, that you need.

Zoë Lenkiewicz: That's it. No, I think that's a great answer. In my experience, like, you know, a waste data project, if you like, it's not something that happens overnight, is it? It is not a matter of building a spreadsheet and the data will come and we'll know exactly how to use that data, and we'll be making really informed decisions and saving a load of money. It's not like that, is it? We have to, you know, it's kind of an iterative process, so bit by bit, you can build to it and then get, you know, comparing different data sets, seeing where the value of that data is for your decision making, and then drilling down into that more and more so that you can you know, save more money, run a more efficient service, achieve better targets, and so on.

Pushkar Pradhan: Yeah, I think. just to, sorry, quickly, Zoë, add on to that, it's also. when you say an iterative process, sometimes you may not initially know what data you need. So it's something that may only become clear through observation or when a problem arises. Then you realize that maybe I should have collected that data, and then you go back to the drawing board. And that is very normal. It's not a fault, it's just the way the entire thing works. Yeah, exactly, yes.

Zoë Lenkiewicz: Absolutely, absolutely, or you do a survey and then realize that you asked a question the wrong way and the answers that you've got, yeah, yeah, data collection is both an art and a science. And yeah, okay, so Laura in the Q&A. Thank you so much for your question. Laura has asked, could you give insight on the gap of data availability of urban versus rural areas? Should one prioritise urban areas where more waste is generated in a smaller area or rural areas where service provision might be trickier and people might be more likely to be dumping and burning their waste because there is no other alternative.

Pushkar Pradhan: I think, see, it depends, both areas need support, right? Let's put it that way. For the urban part of it, I think there are a lot of players that are already there. So if you want to, like, make a mark My suggestion would be, like, focus on tier-three cities, because that's where, you know, the impact can be more. You can. you don't have to look at a small village, but you can group areas into clusters, and then combine them together, because these guys are looking at someone who can manage their waste, and you can set up decentralised units, which is, like, covering a group of, let's say, ten villages or areas and then scale it up from there. Because even at the urban level, the same thing happens, right? We. there's a local level aggregator, there's a second-level aggregator, then it goes to the big level aggregator, then it goes to the recycler. So there are multiple chains that come in.

You could look at both options to be adopted. The type of waste is will differ between rural and urban areas. So your solutions will also not be the same, right? So you'll have to look at what solutions and approaches are appropriate there. Collection may not require sending a vehicle every day to collect the waste. A periodic service may be sufficient.

Zoë Lenkiewicz: Yeah, great. So having that data really helps you to design a system around the actual context rather than assumptions that we might make. And so going back to another point that you were making earlier as well about finance. We also discussed how data can help you attract finance for your waste management system, and I always feel like it's kind of chicken and egg, because if we're not managing our waste, how can we have any data on it? And if we don't have any data on it, and we don't know what waste we've got, we cannot secure the finance then to manage it better. So it is kind of chicken and egg, and I think that oftentimes councils get a bit hung up on this need for perfect data.

Oh, we need to apply for funding to get consultants to produce perfect data so that we can put that in a proposal for more funding, and it's like this endless journey of trying to get funding in. But I think quite often there are things. there are lower cost ways that councils. So I'm thinking particularly now of the smaller and more rural councils, there are ways that they can get data and work together, first of all, you know, because you're more likely to attract funding if you can show that you are working in partnership with neighbouring authorities and then there are also ways that they can get enough data to make a start without needing outside consultants to spend $20,000 on a waste composition study, you know, for a lot of councils, that is just, you know, it's never going to happen. So, what can. what can councils do if they don't have the budget. You know, what have you seen perhaps?

Are there any examples where you think like, yeah, they did really well, they managed to start without that initial outside funding, and then that helped them attract more money to deliver the service.

Pushkar Pradhan: For any project to start, you need funds coming in, right? And the funds will come if you're able to go with a structured approach and show the impact as well. So that's the reason why clustering helps when you are able to combine several neighbouring places and then show that combined impact. It helps a lot. Second part of it is that, any project should not be, you know, like, so much reliant on grants and subsidies, that after the grant or subsidy is exhausted, the project fails, and that happens invariably multiple times. So the aim must also be to make it sustainable, right, in the long run. Now, carbon credits are one option, but just taking one example over there, but one issue I have observed is that they look at carbon credits as a business, right?

So they are looking at that in a very siloed way and when you do not look at the holistic perspective of assessing whether there is genuine potential to generate a carbon credit. Before you actually, you know, go into all that thing of consultancy fees. I think it can be a very small, quick study to say, what is my potential? Am I able to generate, let's say, $10,000 a year out of this? Or plastic credits, right? So there could be something that can be done, and you have to look at creative mechanisms. I guess. The seventh webinar in this series is looking at sustainable financing, which is where it will be covered quite well to look at creative financing, because we have to get creative, look at how you can involve companies to be part of it, and things like that. Plastic credits is also another area, linking it back to EPR and saying, whether it can offer additional value?

Be creative, identify what sort of funding mechanisms that can be tapped in because there are multiple of them. It's just that, you know our perspective is limited to our, you know the level of thinking, but if we think more broadly, I think it can open up more doors for everyone.

Zoë Lenkiewicz: Yeah, I like that. I like that. Think outside the box. Although carbon credits and plastic credits, they are not guaranteed and you might, you know, you might collect the data and then try to sell the credits, but people don't want to buy retrospective credits. So then you've done the work, but you can't get the money for it. So it's not. It's not straightforward, is it? Which is why diversifying your income streams, whether that's through fees from households, fees from businesses, some corporate social responsibility, some extended producer responsibility, and so on Just to help, yeah, it helps build the resilience then in the financing of your system, but you're quite right, that is another one of the foundations, and that's one that we're going to be covering later on in the series. So I'm not going to labour the point, but, you know, without the data, you're not going to get any of that Yeah, yeah.

Super, right, I'm going to move on to. oh, Karthik's got two questions in here. Hi, Karthik, thanks for joining us. Right, I'm going to deal with your first question first, which is, often, monitoring often focuses on service-level indicators. Any thoughts on benchmarking key operational waste data for cities?

Pushkar Pradhan: Yeah, yeah, I think that's very important because I completely agree with Karthik that a lot of the times it becomes very focused on service inputs, right? So whether my vehicle has gone to a particular house for providing a collection service.

Zoë Lenkiewicz: Yeah, but focused on inputs rather than outcomes. Yeah, yeah.

Pushkar Pradhan: Yeah, not focused on outcomes. So definitely it gets limited to that, but there are a lot of operational KPIs that can be built in. Something that can be as simple as saying that, hey, are my vehicles being deployed on time, on a daily basis? Right? Because if you think about it, and keeping different stakeholders in mind, the municipality has the objective, or has a stated objective, saying that every vehicle has to get deployed, let's say, by 7am in the morning. If you do not, then a penalty may apply. So now there's a, you know, a carrot-and-stick approach. There's no carrot, but there's a stick over there saying that, okay, you have to be able to deploy your vehicles. So, one thing is, of course, you can look at some sort of KPIs that can be made available there. The second part of it is that, but here you're then only servicing the municipality, right?

So municipality is saying that, okay, all my things are being met, which is good. But the waste management company may feel that, hey, I am simply being put under pressure. So how do you turn it around and see whether you can help them save costs, using the example that I was giving about optimising their routes for collection or giving them data which they can go back to the municipality and say that, hey, I don't need to send a vehicle for dry-waste collection on a daily basis. Why not we change the scheduling and say that this is the.. I'll send a vehicle for wet waste, and I'll send a separate vehicle every second day or third day for dry waste. And that could be a good saving. Those savings depend on using the available data. The operator can then present evidence, saying that this proves why the change is needed, and I'm not just asking for it without evidence.

So those things have to be brought in. You need to bring in the value for both stakeholders. If you just make it compliance-driven, right? I mean, it will just be a tick-box exercise and it will just be on paper.

Zoë Lenkiewicz: Yeah, yeah, great. And also there's data that we can bring in about customer satisfaction. Did households get the service that they were expecting every week because that, as we discussed earlier, has a knock-on impact on their participation in the service and their willingness to pay for it as well, right? So it's all connected. Okay, final question from Kartik, and then I've got a kind of forwards-looking question after that, and then we'll look to wrap up. So from your experience, what is the minimum digital infrastructure needed for maintaining a good data record of waste services in a city?

Pushkar Pradhan: I think essentially right now with GPS technology, you could leverage GPS for multiple ways, including, let's say, any sort of mobile applications that are, you know, something that can be integrated with operations. My suggestion always is never to go for capital-intensive investment, because until you understand how the operations work in practice. Over-engineered digital interventions often meet resistance, right? If the intended user will not use it. However sophisticated the tool, it's just going to fail. So keep interventions as simple as possible. See if you can leverage QR instead of RFID tags and stuff like that. Again, when you look at QR, you don't have to create too much administrative overhead, because staff time is required with scanning a QR, etc. So these practicalities must be kept in mind, and which is why I always feel that first look at who the people are, train them, bring them up to speed, and then introduce tools one by one. That might be a way to go ahead.

Zoë Lenkiewicz: I love that, yeah, because I think there's obviously companies that provide very detailed waste data systems, but if you're at the beginning of your waste data journey, that can be quite overwhelming and you don't, you know, you don't have the resources to collect all that data anyway. Yeah, correct. So sometimes it is best simply to start where you are, you know, wherever you are, just start with an Excel spreadsheet, decide, you know, what can we. what data can we get hold of at the moment? Could it be the number of trucks arriving at the dumpsite? Is it, you know, customer satisfaction with the service? Is it open burning incidents? Now, I've been working with UNITAR, the United Nations Institute on Training and Research in The Gambia, and we've been building the country's first-ever waste data system for compliance with the Stockholm Convention.

So we need to identify how much waste is being burned in the open at informal dumpsites or in backyards, or whatever, to estimate the persistent organic pollutants released as a result, yeah, yeah, right. So, but if you're starting with no data, there isn't a weighbridge in the country. You know, a lot of the waste is still collected by donkey and cart. So, you know, where do you start? You have to start with something simple and something that's not going to be overwhelming for the officers who are required to fill that in. So is it going to be daily? Probably not. Weekly reporting is probably the right level and then we're not going to have 50 indicators. We're going to start with like eight or 12 indicators and just see how that goes, test that for six months to a year, see what kind of data we get.

And one really interesting approach from the Stockholm Convention that I would encourage people to use as well is to identify the confidence level that you have in your data. Hmm, wow. Yeah, because sometimes we are making assumptions and guesses, but then those numbers can get hard-baked into models. And if actually we weren't confident about that data in the first place, that uncertainty is lost. You know, suddenly it's hard-baked. It's a fact, and we're basing big investment decisions on something that actually was a guess. So it's important to be recording those confidence levels as well, I think, because that can be as valuable as any other piece of data. So yeah, good to see kind of international best practice and how that can be brought into local municipal systems.

And then also, I think having a dashboard is very helpful so that people can see at a glance current performance and where the red flags are, what really needs our attention, so that we know where to focus the resources that we have. So all of that, really, really useful. Now, we've only got a few minutes left, so very, very briefly, and I'm sorry that we didn't leave much time for this, because I think it's a really interesting one. We are in the age of AI. There's a lot of talk about digital product passports and traceability. So looking five years ahead, Pushkar, where will all of these, you know, very cutting-edge technologies make the biggest practical difference, and what do we need to have in place to be able to use them effectively?

Pushkar Pradhan: That is quite a question. I think AI is something which is going to see an uptake for the next few years because it is a new technology. At some point it's going to become more efficient, including in relation to its carbon footprint, because its carbon footprint is currently very high. So you might be, you know, generating more carbon than the intervention saves, so balance is important, I would say, because a lot of times we try to over-engineer it. and I guess, yeah, see, technology is a lever to be able to achieve something. It's not a solution, yeah? You can leverage technology in that way. So, whatever works is something that you should tap in. In some cases, like, when you have those big MRF plants and you have robotics, computer vision and analytics. It makes sense because they are huge.

But for decentralised facilities, you may not need such a high-tech solution, because then it also is a balance between generating employment locally and sharing the benefits locally and using technology. So I guess balance will be important. AI is not going away, but the question is how we can use it productively and to achieve our objectives in a better way. And if that is something like, having a digital product passport, but it need not be used for every product, it can be for high-value or priority products, like electronic waste or whatever. Starting there may deliver real benefits. You don't need a QR-coded EPR system for a packet of biscuits, right? So it's as simple as that.

Zoë Lenkiewicz: Yeah, no, I love that. Thank you, Pushkar. To summarise that back to you, it's like, use technology proportionately, right? Do not go overboard. Consider the value of the information that you're actually going to get from that, and keep the focus on the actual service, on the outcomes, rather than on just collecting data for the sake of it. And I think that, yeah, there's.. I mean, digital product passports, at the moment, the focus hasn't been so much on what happens when that product becomes a waste, particularly if it's moved, for example, out of the European Union and into a region that doesn't have you know, such high-tech sorting systems and so on, you know, are we able to carry the value of that data across to wherever that product or packaging becomes a waste? Or, you know, or are we just transferring the burden? And I think it's really important that we get that balance right.

Pushkar Pradhan: Just if I can add one quick point, because I know time is short, but if we are able to bring in something that supports recycling later, which means that if a product code can provide its composition so that I can break it down and then send it for recycling, I think that's where technology can also help a lot.

Zoë Lenkiewicz: Yeah, yeah, so that's it. You heard it here first, folks. All of this is going to support the circular economy at a deeper and more international level. It doesn't matter whether we are in big cities or rural areas. There's always something we can do to improve the situation using the data that we're able to collect where we are, and start where we are rather than reaching immediately for something, you know, really, really high tech. If we haven't got any data yet, just start where we are, proceed step by step, and build up your evidence base like that. Thanks a lot, Pushkar. Really, really appreciate you joining us and showing how evidence can move from a baseline study into planning, accountability, and everyday service improvement, and ultimately to support a circular economy. Your insights have been hugely valuable. Thank you to everybody who has been here. We've had another 60 people join us today, which has been wonderful.

Thank you for your engagement and all the questions. I'd also like to thank Swetha and be Waste Wise for bringing us together and once again to everybody who joined us today. Your poll responses, questions and contributions have, as ever, made this a much richer conversation. One message I hope we take away is that limited data doesn't mean doing nothing. It means using the best evidence available, being clear about uncertainty, testing the assumptions that carry the greatest risk, and then building review and learning into the system from the start.

Pushkar Pradhan: Thank you, Zoë.