Track State Tipping Fees and Collection Costs with Waste Management Data

Introduction

Long before spreadsheets, dashboards, and APIs, professionals trying to understand solid waste costs often operated in the dark. Waste hauling rates, disposal charges, and landfill tipping fees were negotiated locally, posted on bulletin boards, or buried in municipal documents. For anyone seeking an accurate, timely view of disposal costs by state or region, the answers were scattered and slow to arrive. Decisions around pricing, budgeting, and market entry typically relied on word-of-mouth, phone calls to facilities, and painstaking manual surveys that took weeks or months to complete.

When there was little or no data, the industry turned to antiquated methods such as mailed questionnaires, anecdotal dealer intelligence, and back-of-the-envelope calculations. City councils and procurement officers sometimes waited until the end of a fiscal year to piece together how landfill charges had shifted. Haulers tracked routes on paper and estimated fuel impacts with rough averages. Investors and consultants were often left to infer market dynamics from limited public filings and press releases. In many cases, the true picture of state-by-state tipping fees and collection charges remained opaque.

The advent of connected devices, GPS-enabled fleets, and cloud-based billing systems changed everything. As sensors and routing software became mainstream, the waste ecosystem started generating a comprehensive footprint of what was collected, where it went, and how much it cost. Facilities increasingly stored every transaction, weigh ticket, and fee schedule in databases. Municipalities digitized contracts, bid tabulations, and service-level agreements. The result was a rich lattice of cost, volume, and performance metrics that could finally be analyzed in near real time.

Today, powerful external data sources and unified data platforms allow decision-makers to monitor landfill pricing by state, compare disposal options across regions, and model cost curves under different fuel and commodity scenarios. Waste collection fees that once took months to verify can now be tracked with frequent updates, and tipping fee trends can be evaluated across years of history. Pricing strategy, capital planning, and contract negotiations benefit from this continuous intelligence.

Equally important is the breadth of information now available across many categories of data: operational industry data, government and regulatory filings, municipal procurement records, billing and utility data, geospatial and fleet telematics, commodity price benchmarks, environmental permits and capacity records, and more. Each dataset brings its own strengths and nuances, creating a mosaic of insights about disposal costs, collection fees, and waste volumes.

In short, data has transformed a reactive, lagging picture into a proactive, real-time view of solid waste economics. Instead of waiting weeks or months to understand changes in fees, operators, analysts, and policymakers can act quickly—with confidence—thanks to comprehensive, continuously refreshed information and robust data search capabilities.

Waste and Recycling Operations Data

History and evolution

Operational waste and recycling data emerged as haulers, material recovery facilities (MRFs), transfer stations, and landfills digitized weigh tickets, fee schedules, and route performance. Early on, this information was siloed in local systems and paper archives. Over time, standardized reporting, cloud platforms, and centralized analytics stitched the data together into a coherent historical record of charges, volumes, and service levels. The result is a deep well of disposal cost history, including state-level tipping fee ranges, average gate rates, and fee adjustments over time.

What this data includes

Core elements typically include landfill tipping fees by facility and region, transfer station charges, fuel surcharges, contamination or special handling fees, and municipal collection pricing such as cart rates, bulky item fees, and commercial dumpster service tiers. Some sources incorporate inbound and outbound weight data, commodity residue percentages, contamination rates, and route efficiency statistics.

Who uses it

Historically, waste haulers, facility operators, and municipal managers used this information for operational optimization, budgeting, and compliance. Investors, lenders, and consultants later tapped into these datasets to model profitability, forecast rates, and evaluate market entry or M&A opportunities. Today, developers, site selectors, and even sustainability teams rely on operational waste data to guide infrastructure placement and circular economy initiatives.

Technology advances that enabled it

Cloud-based scale software, API-enabled billing systems, mobile route apps, and data warehousing have all accelerated the collection and normalization of operational data. Digitization of weigh tickets, OCR of historical invoices, and integration of ERP records with CRM and contract documents have made it easier to build a continuous time series of fees and volumes. The growing ubiquity of telematics has layered in route and distance context, enriching the cost story.

Why the data is accelerating

More facilities, municipalities, and haulers now capture detailed transactions at the point of scale, while frequent price adjustments and surcharges necessitate timely updates. As new disposal options, recycling technologies, and extended producer responsibility (EPR) policies emerge, the volume and granularity of pricing and operational metrics grow in tandem.

How it illuminates tipping fees and collection costs

Operational data provides direct visibility into landfill tipping fees and municipal collection charges across states. It supports benchmark comparisons, shows how rates respond to diesel swings or capacity constraints, and uncovers fee differentials between urban and rural areas. Analysts can segment by waste stream—municipal solid waste (MSW), construction and demolition (C&D), organics, or special waste—to understand pricing dynamics by material type.

Specific use cases

State-by-state benchmarking: Compare average and range of tipping fees to identify competitive disposal markets.
Historical trend analysis: Track year-over-year fee changes to anticipate budget impacts and renegotiation timing.
Surcharge and fee breakdowns: Isolate fuel surcharges, contamination charges, and special handling fees to model all-in costs.
Route optimization and costs: Marry route data with facility prices to compute the true delivered cost per ton.
Contract pricing alignment: Validate whether collection rates reflect disposal cost trends and market benchmarks.

With continually updated records, analysts can model scenarios under varying volumes, contamination rates, and transfer station usage. This is foundational data for anyone serious about tracking disposal costs and collection prices at a granular level.

Government and Regulatory Filings Data

History and evolution

Public agencies have long tracked solid waste infrastructure through permits, reports, and regulatory filings. Historically, these were paper-intensive and difficult to access. As governments modernized records management and compliance systems, more information became digitally accessible, including fee schedules, environmental reporting, and statewide surveys of disposal costs.

What this data includes

Datasets often contain statewide averages of landfill tipping fees, facility-level rate schedules, permitted capacities, tonnage reports, and occasional special studies on disposal pricing. Some states publish periodic reports summarizing MSW costs, trends, and policy impacts, while others offer facility registries with links to posted rates and operational details.

Who uses it

Policy makers, municipal analysts, and academic researchers use government datasets to measure affordability, equity, and environmental outcomes. Private-sector stakeholders—including haulers, facility developers, and investors—leverage these records for market entry analysis, pricing strategy, and risk assessment tied to regulation and capacity.

Technology advances that enabled it

Open data portals, standardized reporting templates, and digitized permitting workflows have made state-level tipping fee intelligence far more accessible. Advances in data scraping, data validation, and interoperability have also made it easier to stitch together multi-state views for comparative analysis.

Why the data is accelerating

Environmental transparency, evolving recycling mandates, and EPR policies push agencies to collect and publish more detailed cost and performance data. In parallel, the emphasis on resilience and greenhouse gas mitigation increases the frequency and breadth of public reporting tied to solid waste management economics.

How it illuminates tipping fees and collection costs

Government datasets provide authoritative snapshots of disposal costs, often including long time series. They help identify policy-driven inflection points—such as fee increases following capacity constraints or regulatory shifts—and support statewide models of affordability and access. These datasets also offer critical context on facility permitting and remaining capacity, which heavily influence fee trajectories.

Specific use cases

State average vs. facility-level fees: Analyze the spread between statewide averages and individual landfill rates.
Policy impact assessment: Quantify how new mandates or bans affect disposal prices over time.
Capacity-informed pricing: Correlate permitted capacity and closure schedules with fee changes.
Regional cross-border dynamics: Identify disposal price differentials that drive interstate waste flows.
Historical normalization: Build inflation-adjusted time series to evaluate real changes in tipping fees.

For teams triangulating disposal costs and crafting forecasts, regulatory data is an essential anchor that complements private operational data and market benchmarks.

Municipal Procurement and Contract Data

History and evolution

Municipalities have always managed solid waste through contracts—collection, hauling, processing, and disposal. Historically, bid documents, RFPs, and contracts were scattered across city websites or filing cabinets. As e-procurement platforms spread, these records became more accessible and structured, revealing pricing, service standards, and escalation clauses that shed light on local collection and disposal economics.

What this data includes

Procurement datasets often include bid tabulations, selected vendor pricing, contract terms, escalation indices (e.g., diesel indices or CPI linkages), service zones, cart and container sizes, contamination penalties, and disposal arrangements. Some contracts disclose landfill tip rates or formulas used to pass through disposal costs to the municipality.

Who uses it

City managers, procurement officers, and public works teams use this data to ensure competitive, transparent pricing. Private haulers and facility operators benchmark against winning bids to adjust strategy. Investors, consultants, and market researchers analyze these records to understand pricing power, route density effects, and the impact of service requirements on cost.

Technology advances that enabled it

Electronic RFP platforms, standardized bid forms, and searchable contract repositories have made it easier to assemble large, multi-year corpora of municipal contracts. Text analytics tools and contract intelligence solutions further accelerate extraction of rates, escalation factors, and service scope.

Why the data is accelerating

As municipalities emphasize transparency and performance, they publish more contracts, amendments, and bid results. The shift from paper to digital has created a growing, analyzable archive that illuminates how collection fees evolve and how disposal costs are treated within municipal agreements.

How it illuminates tipping fees and collection costs

Contract data reveals the mechanics behind collection fees: how routes are structured, how disposal is priced or passed through, and how surcharges are triggered. It also discloses escalation formulas, enabling sophisticated models that project future collection fees under varying inflation and fuel scenarios. Contract geography and service density provide additional insight into cost-to-serve variations across neighborhoods and states.

Specific use cases

Bid benchmarking: Compare winning bids and fee structures across similar municipalities to identify market rates.
Escalation modeling: Quantify how diesel indices or CPI lead to fee changes over multi-year terms.
Scope-cost mapping: Tie service requirements (e.g., weekly organics, bulky pickup) to collection fee differentials.
Disposal pass-through analysis: Assess how landfill tipping fees are incorporated into municipal pricing.
Geospatial rate variance: Analyze fees across service zones to understand density and distance impacts on price.

For teams seeking a ground-level understanding of collection fees, procurement and contract data offers unparalleled detail on how prices are constructed and adjusted.

Utility and Billing Data

History and evolution

In many cities, solid waste is billed alongside water, sewer, or other municipal services. Historically, these records were printed on monthly statements and stored locally. With the transition to digital billing platforms and customer portals, municipalities and service providers now maintain granular data on fees by customer class, cart size, and service frequency.

What this data includes

Utility and billing datasets may include residential cart rates by size (e.g., 32/64/96-gallon), commercial dumpster service tiers by yardage and frequency, recycling and organics add-on fees, special pickup charges, and delinquency statistics. Some datasets capture adjustments linked to disposal cost changes or new program rollouts.

Who uses it

Finance departments, rate analysts, and operations leaders rely on billing data to forecast revenue, plan rate cases, and monitor affordability. Consultants and researchers use it to benchmark collection fees, while consumer advocates and NGOs track equity and access.

Technology advances that enabled it

Cloud-based customer information systems (CIS), automated meter and service tracking, and integrated payment portals have improved the timeliness and granularity of billing data. APIs now make it possible to aggregate and analyze trends across multiple jurisdictions quickly.

Why the data is accelerating

As cities expand service offerings—organics, bulky pickup, on-demand collection—billing systems capture more line items and attribute codes. This enriches the analytical signal on how collection fees evolve at the account level, including the impacts of contamination charges and service changes.

How it illuminates tipping fees and collection costs

By linking changes in billed collection fees to known disposal cost shifts, analysts can back into cost pass-throughs and margin structures. Differences across customer classes and service configurations highlight operational drivers of price. With sufficient history, one can isolate seasonality, program adoption effects, and elasticity to fee changes.

Specific use cases

Rate benchmarking: Compare residential and commercial rate tiers across cities and states.
Pass-through detection: Identify billing adjustments tied to changes in landfill tipping fees.
Program impact analysis: Measure how new recycling or organics programs affect total collection cost per household.
Affordability and equity: Evaluate fee burdens by neighborhood or customer class.
Delinquency risk monitoring: Track non-payment patterns when collection fees increase.

Billing datasets provide the most direct, customer-level view of how collection pricing is set and evolves—critical for grounding strategy in real, observed charges.

Geospatial and Fleet Telematics Data

History and evolution

As routing software and GPS trackers became standard in commercial fleets, waste operators gained unprecedented visibility into route performance, mileage, and time-on-route. Simultaneously, geospatial datasets mapped landfills, transfer stations, MRFs, and service territories. Together, these streams enabled precise calculations of distance-to-disposal and cost-to-serve.

What this data includes

Telematics datasets include GPS traces, idle time, speed, mileage, stop counts, and route adherence. Geospatial layers cover facility locations, permitted boundaries, road networks, traffic patterns, and service polygons. Some datasets add live or historical congestion indices and truck-restricted routes.

Who uses it

Operations leaders, dispatchers, and network planners use telematics to optimize routes and reduce fuel spend. Strategy teams, investors, and site selectors evaluate how facility placement affects hauling costs. Municipalities apply geospatial analysis to balance service zones and minimize travel to disposal sites.

Technology advances that enabled it

Low-cost GPS, 4G/5G connectivity, cloud routing engines, and geocoding services made it feasible to collect, store, and analyze vast route datasets. Integration with dispatch and scale systems now ties mileage and time to actual disposal events and fees.

Why the data is accelerating

More vehicles carry connectivity and sensors, while facilities publish richer geospatial metadata. The growth of traffic analytics and high-resolution road networks enables highly accurate estimates of haul time and delivered cost per ton, even before a route is run.

How it illuminates tipping fees and collection costs

Disposal costs are more than a posted tipping fee; they include the cost to get to the gate. Telematics and geospatial data quantify that component precisely. Analysts can model the true all-in disposal expense under different facility choices, traffic conditions, and shift lengths—then align those insights with collection rates and service levels.

Specific use cases

Delivered cost modeling: Combine tipping fees with fuel, driver time, and maintenance to compute delivered cost per ton.
Facility selection optimization: Evaluate multiple landfills or transfer stations to minimize all-in disposal costs.
Route redesign: Rebalance service zones to reduce disposal travel and lower collection fees over time.
Peak congestion planning: Quantify the cost impact of rush hour versus off-peak disposal trips.
New site feasibility: Assess where to place transfer stations to reduce downstream hauling expense.

This location-aware lens connects the dots between a posted fee and the real cost customers experience—vital for pricing, network design, and competitive strategy.

Commodity and Macroeconomic Price Data

History and evolution

Waste economics are tightly linked to commodity markets and macro factors. When recyclables like OCC, mixed paper, metals, and certain plastics fetch higher prices, net program costs shift. Similarly, diesel prices and inflation indices flow through to surcharges and escalators. Over the last decade, commodity market data, energy price indices, and inflation metrics have become integral to waste cost modeling.

What this data includes

Datasets cover benchmark prices for recyclable commodities, diesel and other fuel indices, CPI and PPI measures, and sometimes regional energy cost adjustments. Many contracts explicitly tie collection fee escalators to these indices, making them essential inputs for forecasting.

Who uses it

Finance teams, pricing analysts, and procurement leaders track commodity and fuel indices to anticipate changes in both disposal and collection fees. Investors and consultants use these measures to stress test models and evaluate exposure to price swings.

Technology advances that enabled it

Real-time feeds, market data APIs, and improved price discovery have increased the timeliness and granularity of commodity and energy indices. Data blending tools make it easier to merge these series with operational and billing data.

Why the data is accelerating

As recycling programs evolve and fuel price volatility persists, stakeholders demand tighter integration of market indices into planning and contracts. This produces more frequent updates, longer histories, and richer cross-commodity views.

How it illuminates tipping fees and collection costs

While landfill tipping fees are primarily facility- and capacity-driven, macro factors influence surcharges, transfer costs, and overall service pricing. Commodity prices affect net program costs in recycling and organics, while diesel and inflation indices feed directly into escalators. Together, these datasets translate market conditions into concrete pricing implications.

Specific use cases

Escalator forecasting: Predict collection fee changes tied to CPI, PPI, and diesel indices.
Recycling program ROI: Assess how commodity price swings alter program cost per household or ton.
Stress testing: Model worst-case and best-case fee scenarios under extreme fuel or commodity movements.
Cross-state comparisons: Normalize disposal cost changes across regions by adjusting for macro index differences.
Budget planning: Align annual budgets with expected index trends to avoid mid-year surprises.

By integrating commodity and macroeconomic data, stakeholders can build dynamic, resilient pricing strategies that respond to market reality rather than static assumptions.

Environmental Permits, Capacity, and Performance Data

History and evolution

Landfill economics depend heavily on permitted capacity, closure timelines, and environmental performance. Historically, these details were locked in technical filings and environmental reports. As agencies digitized permits and compliance documentation, analysts gained clearer visibility into capacity constraints and infrastructure investment needs that often drive tipping fee changes.

What this data includes

Permits, expansions, remaining capacity estimates, closure dates, gas collection system details, leachate management practices, and monitoring results. In some cases, regulatory disclosures also include infrastructure investments and major maintenance that can influence gate rates.

Who uses it

Facility operators, environmental engineers, and compliance teams track this data closely. Strategists and investors analyze capacity and closure risk to anticipate fee trajectories and infrastructure development opportunities. Municipal planners use it to ensure long-term disposal security.

Technology advances that enabled it

Digitized permitting systems, document search, and geospatial tagging have made it easier to link environmental records with facility attributes and pricing data. Visualization tools and spatial analytics help non-technical users interpret complex technical information.

Why the data is accelerating

Greater emphasis on environmental performance and greenhouse gas mitigation drives more frequent and detailed reporting. As infrastructure ages and urbanization pressures capacity, stakeholders require better forward visibility, prompting richer data publication.

How it illuminates tipping fees and collection costs

Capacity constraints can lead to fee increases or changes in disposal patterns that raise delivered costs. Environmental upgrades may also influence gate rates. Understanding expansion timelines, remaining capacity, and compliance investments helps forecast tipping fees and informs collection pricing strategies linked to disposal options.

Specific use cases

Capacity-driven pricing forecasts: Model fee increases as remaining capacity declines.
Closure risk mapping: Identify regions where closures may force longer hauls and higher collection costs.
Expansion scenario planning: Evaluate how new cells or facilities could stabilize fees.
Compliance cost pass-throughs: Anticipate fee impacts from mandated environmental upgrades.
Security-of-disposal analysis: Assess long-term viability of disposal options in multi-decade plans.

Blending environmental and capacity signals with pricing data enables proactive planning—avoiding shock adjustments and preserving service affordability.

Survey, Panel, and Research Data

History and evolution

Even with extensive operational and public datasets, surveys and panels remain invaluable for filling gaps and capturing nuance—such as non-posted fees, policy expectations, and sentiment on future pricing. Historically done by mail or phone, modern survey platforms and research methodologies produce faster cycles and more representative samples.

What this data includes

Facility-reported tipping fees, fee components, accepted materials, policy changes, and capacity outlooks. Hauler and municipal surveys may cover collection rates, service levels, contamination experiences, contract structures, and expected escalations.

Who uses it

Market researchers, consultants, trade associations, and strategy teams rely on survey data to validate and calibrate models built from operational and public data. Investors and lenders use it to cross-check assumptions and assess near-term risks.

Technology advances that enabled it

Online panels, programmatic sampling, and survey analytics platforms speed up collection and improve data quality. Natural language processing helps extract structured insights from open-ended responses and contract text.

Why the data is accelerating

As markets move faster, frequent pulse checks capture emerging shifts—new surcharges, contamination thresholds, or operational bottlenecks—that have not yet appeared in official datasets. This velocity makes surveys a powerful complement.

How it illuminates tipping fees and collection costs

Surveys help pinpoint current gate rates and fee components, estimate near-term changes, and contextualize why fees move—capacity, policy, or market dynamics. They also capture programmatic changes in collection that flow into customer pricing.

Specific use cases

Price validation: Cross-verify posted fees against operator-reported rates.
Forward-looking signals: Capture expectations for fee adjustments over the next 6–12 months.
Policy readiness: Gauge how new mandates could alter costs.
Contamination insights: Understand surcharges and program changes impacting collection fees.
Benchmark completeness: Fill gaps in regions with sparse public data.

For organizations training predictive models, carefully curated survey responses can also serve as high-value training data, especially when blended with structured operational series and contract metadata.

From Data to Decisions: Practical Steps

Building a unified view

To translate disparate datasets into actionable insight, establish a schema that aligns facility-level tipping fees, municipal collection rates, route distances, and index-linked escalators. Use data dictionaries to harmonize fields, and retain original sources for auditability. When possible, augment with facility coordinates and service polygons to power spatial analyses.

Enabling real-time monitoring

Set up automated pipelines to ingest updates from external data feeds, open portals, and internal systems. Trigger alerts when fees change beyond thresholds, contracts hit escalation dates, or diesel indices swing. Maintain both nominal and inflation-adjusted series for clear storytelling.

Layering predictive analytics

Incorporate regression or time-series models that relate fees to capacity, traffic distance, commodity prices, and policy events. If using AI or machine learning, emphasize feature engineering around contract terms, route metrics, and macro indices, and stress test with scenario modeling.

Governance and provenance

Document lineage, refresh cadence, and definitions for every metric. This ensures confidence in comparisons across states and time periods, and it supports transparent communication with stakeholders ranging from public boards to investors.

Discovering more sources

Continuously scan new types of data and research outputs to enrich your model. Use purpose-built data search tools to identify feeds that cover underrepresented regions or niche materials streams.

Conclusion

The ability to track disposal costs and collection fees across states has moved from a frustrating, manual endeavor to a precise, timely discipline. Operational industry datasets, public regulatory records, municipal contract archives, billing systems, geospatial and telematics signals, and market indices now converge to create a dynamic picture of waste economics.

This transformation empowers professionals to see changes as they happen, not months later. Fee updates, surcharge introductions, and capacity-driven shifts are captured quickly, enabling better budgeting, pricing, and capital planning. The days of relying solely on phone calls and anecdote are fading; data-backed decisions are ascendant.

Organizations that embrace a data-driven culture will outpace those that do not. Establishing a robust discovery process for external data, blending it with internal records, and maintaining trusted time series are now table stakes. The sophistication of your models—the ability to incorporate capacity, traffic, commodity, and policy signals—will define your competitive edge.

As more entities look to monetize their data, the waste ecosystem will gain access to new streams: anonymized route efficiency benchmarks, dynamic queue times at facilities, real-time scale data, and even sensor-based contamination detection. Municipalities and operators are increasingly aware that historical archives—once dormant—can generate value when responsibly shared.

Looking ahead, we can expect richer integrations: contract intelligence tools that parse escalation clauses at scale, facility digital twins tied to capacity and compliance metrics, and predictive engines that unify all these inputs. Advances in Artificial Intelligence will continue to help extract, normalize, and forecast from unstructured and semi-structured documents, but—as ever—the real advantage will come from the breadth, quality, and recency of the data itself.

For teams committed to continuous improvement, the path is clear: expand your data perimeter, routinely validate against on-the-ground reality, and keep a living model of state-by-state fees and service costs. With disciplined processes and the right mix of sources, you will not just track the market—you will anticipate it.

Appendix: Who Benefits and What Comes Next

Investors and lenders: Investment committees rely on long, clean histories of tipping fees and collection rates to underwrite projects, acquisitions, and credit lines. By merging operational industry data, procurement records, and macro indices, they can evaluate margin resilience and cyclicality. Timely updates from external data feeds keep models current through market volatility.

Consultants and market researchers: Advisory teams need rapid access to state-by-state benchmarks, contract structures, and capacity outlooks. A repeatable workflow for discovering new categories of data—and transforming them into client-ready insights—defines their value proposition. Survey data helps fill gaps and captures emerging signals.

Municipalities and public agencies: City managers and public works directors use pricing data to structure fair, resilient contracts and to communicate transparently with residents. Billing histories and contract archives inform affordability analysis, while capacity and environmental datasets safeguard long-term disposal security. Many agencies also explore how to responsibly monetize their data to fund modernization.

Haulers and facility operators: Operators live and breathe the numbers—gate rates, surcharges, route costs, contamination penalties. Integrating telematics, operational records, and market indices drives sharper pricing strategies and capital allocation. Competitive intelligence gleaned from contracts and public filings supports smarter bidding and network design.

Insurers and risk managers: Underwriters evaluate exposure to fuel volatility, regulatory shifts, and capacity constraints. Environmental permits and performance histories, combined with macro indices, improve risk scoring and policy design. Claims analytics enriched by route and geospatial data can flag operational hazards before they escalate.

The role of AI and document intelligence: Decades-old contracts, scanned tipping fee sheets, and legacy permits often hide crucial signals. Modern AI and NLP tools can extract rate tables, escalation clauses, and service scopes from unstructured text, transforming dusty archives into decision-grade datasets. For teams building predictive models, curated corpora of contracts and historical filings can serve as powerful training data, especially when blended with structured operational feeds.

Across the ecosystem, the future favors those who cultivate broad data partnerships, maintain rigorous governance, and iterate quickly. As new datasets emerge—from queue-time telemetry to real-time scale transactions—leaders will deepen their understanding of disposal costs and collection fees, turning insight into operational excellence.