This data package includes the underlying data files to replicate the data, tables, and charts presented in Why Trump’s tariff proposals would harm working Americans, PIIE Policy Brief 24-1.

If you use the data, please cite as: Clausing, Kimberly, and Mary E. Lovely. 2024. Why Trump’s tariff proposals would harm working Americans. PIIE Policy Brief 24-1. Washington, DC: Peterson Institute for International Economics.

U.S. tariff rates for 2022 was 1.49%, a 0.02% increase from 2021.

<ul style='margin-top:20px;'>

<li>U.S. tariff rates for 2021 was <strong>1.47%</strong>, a <strong>0.05% decline</strong> from 2020.</li>
<li>U.S. tariff rates for 2020 was <strong>1.52%</strong>, a <strong>12.26% decline</strong> from 2019.</li>
<li>U.S. tariff rates for 2019 was <strong>13.78%</strong>, a <strong>12.19% increase</strong> from 2018.</li>
</ul>Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Data are classified using the Harmonized System of trade at the six- or eight-digit level. Tariff line data were matched to Standard International Trade Classification (SITC) revision 3 codes to define commodity groups and import weights. To the extent possible, specific rates have been converted to their ad valorem equivalent rates and have been included in the calculation of weighted mean tariffs. Import weights were calculated using the United Nations Statistics Division's Commodity Trade (Comtrade) database. Effectively applied tariff rates at the six- and eight-digit product level are averaged for products in each commodity group. When the effectively applied rate is unavailable, the most favored nation rate is used instead.

This dataset is restricted, for more information please contact the author. Data were collected from multiple sources:The Electricity & Co-Generation Regulatory AuthoritySaudi Electricity companyWeb news article (2015, December 28). Increase of Fuel, Electricity and Water prices. Retrieved from https://akhbaar24.argaam.com/article/detail/255091accessed on March 22, 2018.In October 1984, the government adopted a Tariff that increased with increasing consumption. The changes of Tariffs started in November 1984.Tariff approved by Council of Ministries 170 and become effective in October 2000. This Tariff remained effective for approximately ten years The residential, agricultural, mosques, and charitable societies remained unchanged till 2018In 2010, a new tariff for government, commercial, and industrial consumption came into force, this was adopted by a decision of ECRA's board, to set tariffs for non-residential consumption with an upper limit of SR0.26/kWh.In 2015, the total value of electricity consumed by the residential sector was worth about 38 billion U.S. dollars.In 2018, the Council of Ministers has approved gradual revision of energy prices in the Kingdom including changes to electricity tariffs effective from Jan. 1. 2018, the Electricity and Cogeneration Regulatory Authority (ECRA) announced that new prices will take effect on January 1st, 2018.source: ECRACitation: Alghamdi, Abeer. 2018. “Changes in Saudi Arabia Electricity Prices.” [dataset]. https://datasource.kapsarc.org/explore/dataset/electricity-prices-in-saudi-arabia/information/.

The ad valorem equivalent (AVE) of non-tariff measures (NTMs) is the uniform tariff that will result in the same trade impacts on the import of a product due to the presence of the NTMs. In other words, the AVEs represent the additional costs that the presence of NTMs has on imports. The AVE estimation is based on data in the TRAINS database. To minimize time inconsistencies, the analysis utilizes a reduced sample of NTMs data collected between 2012 and 2016. The data is transformed in a cross section database spanning about 40 importing countries plus the European Union, about 200 exporting countries. AVEs are estimated at the HS 6 digit classification and on a bilateral basis. Additional data required for the estimation originates from TRAINS (tariffs), the United Nations Comtrade database (trade flows) and from the World Development Indicators database. The AVEs of NTMs presented here are based on the estimation method developed in Kee and Nicita (2018), which in turn, builds on the work of Kee, Nicita and Olarreaga (2009). The AVE of an NTM indicates the proportional rise in the domestic price of the goods to which it is applied, relative to a counterfactual where it is not applied.

How Tariffs Are Impacting the Economy

Tariffs on imported technology products, including hardware and software components critical for database security solutions, are impacting the economy by raising production and operational costs. These increased costs often cascade down to consumers and enterprises, resulting in higher prices for security solutions and services. Tariffs disrupt supply chains by causing delays and logistical challenges, further increasing expenses and slowing the deployment of essential security infrastructure.

➤ Discover how our research uncovers business opportunities @ https://market.us/report/database-security-market/free-sample/
(Use corporate mail ID for quicker response)

The uncertainty stemming from fluctuating trade policies causes companies to hesitate in committing to large-scale investments in cybersecurity. However, tariffs can also stimulate domestic manufacturing and software development, potentially reducing dependency on foreign suppliers and enhancing economic resilience. Over time, this shift could lead to increased innovation and job creation within local markets, balancing out short-term economic strains.

https://scoop.market.us/wp-content/uploads/2025/05/US-Tariff-Impact-on-Market-2025-2.png" alt="US Tariff Impact on Market - 2025" class="wp-image-54546">

This table contains figures on consumer prices for electricity and gas. These are subdivided into transport prices, delivery prices and taxes (including and excluding VAT). The figures are published as weighted average monthly prices. The average energy prices published here are the prices as used for the consumer price index (CPI) up to and including May 2023. Prices of new contracts were observed at the CPI. Contracts that were offered by energy companies in previous periods, but not in the relevant reporting period, have been mathematically continued and included in the calculation of the average tariff. The average prices in this table may therefore deviate from the average prices that Dutch households pay for energy. Data available from January 2018 to May 2023. Status of the figures: The data are final. Changes as of July 20, 2023: None, this table has been discontinued. Due to a change in the underlying data and associated method for calculating average energy rates, a new table will be published on 20 July. See section 3. Changes as of February 13, 2023: From January 2023, the average delivery rates will not be published. With the introduction of the price cap, the average energy rates (supply rates) of fixed and variable energy contracts together were very useful to calculate a development for the CPI. As a price point, however, they are less useful. The delivery rates from January 2023 to May 2023 are published in a custom table based on the data for new variable contracts. When will new numbers come out? Not applicable anymore.

Vermont pole owning electric utilities Green Mountain Power (GMP) and Vermont Electric Coop (VEC) have adopted similar amendments to their pole attachment tariffs, approved by the Vermont Public Utility Commission (PUC) to provide a credit toward the cost of utility pole makeready costs. These tariffs can be found at the links below:GMP Tariff: Vermont PUC docket 21-0546VEC Tariff: Vermont PUC docket 21-0807From the Tariffs: The Program will be offered for three years and will provide support on makeready costs to the first broadband provider that proposes to offer broadband service to an unserved location (defined as service less than 4/1) provided that location is not part of an application submitted prior to the effective date of the Tariff Rider, nor included in any State or federal subsidy program award that has been issued or applied for prior to that date. The maximum amount GMP will credit the provider for each unserved location within a pole attachment application is $2,000 but that amount may be reduced depending on a true-up after actual costs are known. In no event can an application’s make-ready cost reduction exceed the actual cost of work.This preliminary dataset relates locations considered to be eligible for this program. These locations were drawn from the 2021 Broadband Status dataset. From that dataset, locations lacking service at 4/1 Mbps and that we not part of the Connectivity Initiative 2020 or the FCC Rural Digital Opportunities Fund grant programs were selected for this dataset. The table below lists the quantity of eligible locations in this dataset in each Vermont town. TOWNNAME GMP VEC Total

ADDISON 23

23

ALBANY

3 3

ALBURGH

19 19

ARLINGTON 63

63

ATHENS 1

1

AVERILL

8 8

AVERYS GORE

8 8

BAKERSFIELD 4 8 12

BARNARD 4

4

BARNET 42

42

BARRE TOWN 2

2

BARTON

20 20

BELVIDERE

16 16

BENNINGTON 64

64

BENSON 81

81

BERKSHIRE

24 24

BERLIN 46

46

BETHEL 14

14

BLOOMFIELD

49 49

BOLTON 5 2 7

BRADFORD 61

61

BRAINTREE 11

11

BRANDON 45

45

BRATTLEBORO 26

26

BRIDGEWATER 5

5

BRIDPORT 49

49

BRIGHTON

41 41

BRISTOL 12

12

BROOKFIELD 10

10

BROOKLINE 5

5

BROWNINGTON

3 3

BRUNSWICK

30 30

BUELS GORE 3

3

CABOT 13

13

CAMBRIDGE 2 262 264

CANAAN

32 32

CASTLETON 36

36

CAVENDISH 22

22

CHARLESTON

37 37

CHARLOTTE 12

12

CHITTENDEN 50

50

CLARENDON 30

30

COLCHESTER 31

31

CONCORD 92

92

CORINTH 79

79

CORNWALL 11

11

COVENTRY

25 25

CRAFTSBURY

45 45

DANVILLE 107

107

DERBY

87 87

DORSET 40

40

DOVER 3

3

DUMMERSTON 84

84

DUXBURY 46

46

EAST HAVEN 5

5

EAST MONTPELIER 2

2

EDEN

83 83

ELMORE 1

1

ENOSBURGH

1 1

ESSEX 38 9 47

FAIR HAVEN 26

26

FAIRFAX 2 1 3

FAIRFIELD 13 10 23

FAIRLEE 19

19

FAYSTON 13

13

FERDINAND

8 8

FERRISBURGH 95

95

FLETCHER 4 8 12

FRANKLIN

3 3

GEORGIA 12 2 14

GLASTENBURY 1

1

GLOVER

39 39

GOSHEN 12

12

GRANBY 19

19

GRANVILLE 6

6

GROTON 32

32

GUILDHALL 7 2 9

GUILFORD 65

65

HALIFAX 27

27

HANCOCK 9

9

HARTFORD 29

29

HARTLAND 15

15

HINESBURG 3 8 11

HOLLAND

43 43

HUBBARDTON 79

79

HUNTINGTON 1 9 10

HYDE PARK

15 15

IRA 3

3

IRASBURG

10 10

ISLE LA MOTTE

5 5

JAMAICA 69

69

JAY

8 8

JERICHO 4 57 61

JOHNSON

67 67

KILLINGTON 2

2

KIRBY 35

35

LANDGROVE 1

1

LEICESTER 12

12

LEMINGTON

32 32

LEWIS

9 9

LINCOLN 6

6

LONDONDERRY 18

18

LOWELL

17 17

LUDLOW 4

4

LUNENBURG 74

74

LYNDON

1 1

MAIDSTONE

80 80

MANCHESTER 42

42

MARLBORO 34

34

MARSHFIELD 27

27

MENDON 25

25

MIDDLEBURY 37

37

MIDDLESEX 3

3

MIDDLETOWN SPRINGS 3

3

MILTON 28 1 29

MONKTON 16

16

MONTGOMERY

62 62

MONTPELIER 1

1

MORETOWN 8

8

MORGAN

56 56

MORRISTOWN

13 13

MOUNT HOLLY 2

2

NEW HAVEN 32

32

NEWARK

10 10

NEWBURY 90

90

NEWFANE 87

87

NEWPORT CITY

7 7

NEWPORT TOWN

46 46

NORTH HERO

3 3

NORTHFIELD 29

29

NORWICH 5

5

ORWELL 131

131

PANTON 7

7

PAWLET 49

49

PEACHAM 28

28

PITTSFIELD 2

2

PITTSFORD 63

63

PLAINFIELD 6

6

PLYMOUTH 4

4

POMFRET 10

10

POULTNEY 76

76

POWNAL 52

52

PROCTOR 2

2

PUTNEY 71

71

RANDOLPH 3

3

READING 2

2

READSBORO 26

26

RICHFORD

8 8

RICHMOND 1

1

RIPTON 45

45

ROCHESTER 19

19

ROCKINGHAM 40

40

ROXBURY 78

78

ROYALTON 5

5

RUPERT 96

96

RUTLAND TOWN 1

1

RYEGATE 96

96

SAINT ALBANS TOWN

3 3

SAINT GEORGE

1 1

SAINT JOHNSBURY 33

33

SALISBURY 128

128

SANDGATE 47

47

SEARSBURG 17

17

SHAFTSBURY 81

81

SHARON 1

1

SHEFFIELD

35 35

SHELBURNE 19

19

SHELDON 4 6 10

SHOREHAM 55

55

SHREWSBURY 7

7

SOMERSET 14

14

SOUTH BURLINGTON 8

8

SOUTH HERO

8 8

STAMFORD 65

65

STANNARD 2

2

STARKSBORO 15 1 16

STOCKBRIDGE 15

15

STOWE

16 16

STRAFFORD 1

1

STRATTON 41

41

SUDBURY 28

28

SUNDERLAND 20

20

SWANTON

19 19

TOPSHAM 26

26

TOWNSHEND 23

23

TROY

4 4

TUNBRIDGE 8

8

UNDERHILL 4 13 17

VERGENNES 3

3

VERNON 35

35

VERSHIRE 3

3

VICTORY 4

4

WAITSFIELD 26

26

WALTHAM 35

35

WARDSBORO 118

118

WARREN 27

27

WARREN GORE

4 4

WASHINGTON 1

1

WATERBURY 27

27

WATERFORD 76

76

WATERVILLE

145 145

WEATHERSFIELD 38

38

WELLS 14

14

WEST FAIRLEE 2

2

WEST HAVEN 33

33

WEST RUTLAND 3

3

WEST WINDSOR 3

3

WESTFIELD

3 3

WESTFORD 3 7 10

WESTMINSTER 74

74

WESTMORE

2 2

WESTON 8

8

WEYBRIDGE 10

10

WHEELOCK 4 4 8

WHITING 6

6

WHITINGHAM 11

11

WILLIAMSTOWN 9

9

WILLISTON 5 4 9

WILMINGTON 14

14

WINDHAM 4

4

WINDSOR 5

5

WINHALL 2

2

WOODFORD 24

24

WOODSTOCK 48

48

WORCESTER 3

3

Grand Total 4719 1727 6446

The dataset provides the price indices computed for the academic paper "Price and Global Inequality", available at https://www.xavierjaravel.com/papers. The data has been created as part of the project addressing two questions: (1) What are the implications of prices changes for inequality and standards of living? (2) To what extent do the price effects induced by policies alter the cost-benefit analysis of these policies? Despite extensive research, we currently lack detailed data as well as various empirical and theoretical tools to appropriately answer these questions. These questions are fundamental because it is well-known that individuals across the income distribution purchase different baskets of goods and services. Therefore, changes in prices or product availability over time can potentially have an important impact on inequality.

This project asks two questions:

(1) What are the implications of prices changes for inequality and standards of living?

(2) To what extent do the price effects induced by policies alter the cost-benefit analysis of these policies?

Despite extensive research, we currently lack detailed data as well as various empirical and theoretical tools to appropriately answer these questions.

These questions are fundamental because it is well-known that individuals across the income distribution purchase different baskets of goods and services. Therefore, changes in prices or product availability over time can potentially have an important impact on inequality. Likewise, differences in prices across countries can have a profound impact on standards of living across countries.

The few studies that have investigated these questions have used "macro" data (at a high level of aggregation), but I have shown in previous work (Jaravel 2017) that it is crucial to use "micro" data (i.e. very disaggregated data, at the product level) to accurately answer these questions.

We know that policies may have large price effects (see Jaravel 2018 on the price effects of food stamps). For instance, increasing import tariffs is likely to result in higher prices for domestic consumers (which I have started investigating in ongoing work: Borusyak and Jaravel 2017 and Jaravel and Sager 2018). But we do not have a good understanding of how large this effect might be. Likewise, other important policies like income redistribution schemes or monetary policy could have significant effects on prices, which are not well understood currently.

There are two main challenges to answer the two fundamental questions asked in this project. First, it is not easy to properly measure how prices change over time and across countries, because the set of available goods and services is always changing and detailed micro data is required. Second, it is challenging to understand the impact of policies on prices because of feedback loops. For instance, if a given policy makes a particular group of individuals richer, they might change their consumption patterns and start buying a different set of goods or services, which may have an impact on the income of other agents, who in turn will change their consumption patterns, etc.

In this project, I propose to proceed in two steps, tackling each of these two major challenges in turn to advance our understanding of the effects of price changes and of their implications for major policies. The first part of the project aims at addressing three fundamental limitations in the literature on the measurement of "quality-adjusted" price changes (building on Jaravel 2017): (i) limited availability of scanner data across countries; (ii) limited use of hedonic regressions; and (iii) limited understanding of the welfare impact of house prices changes. Using new models and new empirical tools, the second part of the project aims at shedding new light on the welfare impact of three important types of policies, given their price effects: (i) optimal income and commodity taxation; (ii) trade policy (building on Borusyak and Jaravel 2017 and Jaravel and Sager 2018); and (iii) monetary policy.

The various parts of this project constitute a cohesive whole. Taking a multi-faceted approach is the only way of making significant progress on understanding the effects of prices and their policy implications.

This project has a strong potential for impact. In particular, it could change the type of inflation statistics published by national government agencies, as well as the type of standards-of-living statistics across countries published by international organisations. To ensure that the new data and new findings from the project are easily accessible by other researchers, policymakers, think tanks, as well as by the general public, the results and data will be made available online on a dedicated, user-friendly website.

SUMMARY The most complete, highest quality database of EV charging stations across the globe, with everything you want to know regarding charging locations and tariffs. All attributes are available at individual connector level. The perfect input for network planning, pricing analyses, market projections, go-to market strategies, or other analyses.

—

Eco-Movement is the leading source for EV charging station data. We offer full coverage of all (semi)public EV chargers across Europe, North & Latin America, Oceania, and ever more additional countries. Our real-time database now contains about 1,000,000 unique plugs. Eco-Movement is a specialised B2B data provider focusing 100% on EV charging station data quality and enrichment. Hundreds of quality checks are performed through our proprietary quality dashboard, IT architecture and AI. With the highest quality on the market, we are the trusted choice of mobility industry leaders such as Google, Tesla, Bloomberg, and the European Commission’s EAFO portal.

Eco-Movement integrates data from 300+ direct connections with EV Charge Point Operators into a uniform, accurate and complete database. We have an unparalleled set of charge point related attributes, all available on individual charging plug level: from Geolocation to Max Power and from Operator to Hardware and Pricing details. Simple, reliable, and up-to-date: The Eco-Movement database is refreshed every day.

When you are in need of insights, high quality data is more important than ever. Our online Data Retrieval Platform is the easy solution to all your EV Charging Station related data needs. It includes various charts that you can filter and group to your preferences, plus the possibility to download all data (or a selection) in CSV format for analysis in your preferred software, e.g. Tableau or Excel.

Location attributes include coordinates, address, operator, power, connector type, location category, parking type, access type (public / restricted / private), and accepted payment methods. Tariff attributes include price per kWh, per hour charging and/or parking, flat fees, and subscription fees. The reports are available for all countries in our database. The price of the data is dependent on the geographies chosen, the length of the subscription, and the intended use.

Check out our other Data Offerings available, and gain more valuable market insights on EV charging directly from the experts.

ALSO AVAILABLE We also offer EV Charging Station Location & Tariffs Data via a real-time API with information about charging station availability, and can offer a separate CSV report focused specifically on DC station hardware manufacturer and model information.

ABOUT US

Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

Note: Find data at source. ・ Federal and state decarbonization goals have led to numerous financial incentives and policies designed to increase access and adoption of renewable energy systems. In combination with the declining cost of both solar photovoltaic and battery energy storage systems and rising electric utility rates, residential renewable adoption has become more favorable than ever. However, not all states provide the same opportunity for cost recovery, and the complicated and changing policy and utility landscape can make it difficult for households to make an informed decision on whether to install a renewable system. This paper is intended to provide a guide to households considering renewable adoption by introducing relevant factors that influence renewable system performance and payback, summarized in a state lookup table for quick reference. Five states are chosen as case studies to perform economic optimizations based on net metering policy, utility rate structure, and average electric utility price; these states are selected to be representative of the possible combinations of factors to aid in the decision-making process for customers in all states. The results of this analysis highlight the dual importance of both state support for renewables and price signals, as the benefits of residential renewable systems are best realized in states with net metering policies facing the challenge of above-average electric utility rates.This dataset is intended to allow readers to reproduce and customize the analysis performed in this work to their benefit. Suggested modifications include: location, household load profile, rate tariff structure, and renewable energy system design.

Data on water utilities for 151 national jurisdictions, for a range of years up to and including 2017 (year range varies greatly by country and utility) on service and utility parameters (Benchmark Database) and Tariffs for 211 juristictions (Tariffs database). Information includes cost recovery, connections, population served, financial performance, non-revenue water, residential and total supply, total production. Data can be called up by utility, by group of utility, and by comparison between utilities, including the whole (global) utility database, enabling both country and global level comparison for individual utilities. Data can be downloaded in xls format.

Eco-Movement is the leading source for EV charging station data. We offer full coverage of all (semi)public EV chargers across Europe, North & Latin America, Oceania, and ever more additional countries. Our real-time database now contains about 1,000,000 unique plugs. Eco-Movement is a specialised B2B data provider focusing 100% on EV charging station data quality and enrichment. Hundreds of quality checks are performed through our proprietary quality dashboard, IT architecture and AI. With the highest quality on the market, we are the trusted choice of mobility industry leaders such as Google, Tesla, Bloomberg, and the European Commission’s EAFO portal.

Eco-Movement integrates data from 3000+ direct connections with EV Charge Point Operators into a uniform, accurate and complete database. We have an unparalleled set of charge point related attributes, all available on individual charging plug level: from Geolocation to Max Power and from Operator to Hardware and Pricing details. Simple, reliable, and up-to-date: The Eco-Movement database is refreshed every day.

When you are in need of insights, high quality data is more important than ever. Our online Data Retrieval Platform is the easy solution to all your EV Charging Station related data needs. It includes various charts that you can filter and group to your preferences, plus the possibility to download all data (or a selection) in CSV format for analysis in your preferred software, e.g. Tableau or Excel.

Location attributes include coordinates, address, operator, power, connector type, location category, parking type, access type (public / restricted / private), and accepted payment methods. Tariff attributes include price per kWh, per hour charging and/or parking, flat fees, and subscription fees. The reports are available for all countries in our database. The price of the data is dependent on the geographies chosen, the length of the subscription, and the intended use.

Check out our other Data Offerings available, and gain more valuable market insights on EV charging directly from the experts.

ALSO AVAILABLE We also offer EV Charging Station Location & Tariffs Data via a real-time API with information about charging station availability, and can offer a separate CSV report focused specifically on DC station hardware manufacturer and model information.

ABOUT US Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

Lumber fell to 610.64 USD/1000 board feet on June 23, 2025, down 0.87% from the previous day. Over the past month, Lumber's price has risen 2.88%, and is up 33.32% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lumber - values, historical data, forecasts and news - updated on June of 2025.

Aluminum fell to 2,591.50 USD/T on June 27, 2025, down 0.03% from the previous day. Over the past month, Aluminum's price has risen 4.90%, and is up 2.65% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Aluminum - values, historical data, forecasts and news - updated on June of 2025.

SUMMARY The most complete, highest quality data feed with EV charging stations across the globe. Data is sourced directly from the Charge Point network Operators and enriched with dozens of custom attributes. All data is updated daily, and availability of stations is pushed to you real-time.

—

Eco-Movement is the leading source for EV charging station data. We offer full coverage of all (semi)public EV chargers across Europe, North & Latin America, Oceania, and ever more additional countries. Our real-time database now contains about 1,000,000 unique plugs. Eco-Movement is a specialised B2B data provider focusing 100% on EV charging station data quality and enrichment. Hundreds of quality checks are performed through our proprietary quality dashboard, IT architecture and AI. With the highest quality on the market, we are the trusted choice of mobility industry leaders such as Google, Tesla, HERE, Telenav, and A Better Route Planner.

Eco-Movement integrates data from 300+ direct connections with EV Charge Point Operators into a uniform, accurate and complete database. We have an unparalleled set of charge point related attributes, all available on individual charging plug level: from Geolocation to Max Power and from Operator to Hardware and Pricing details. Simple, reliable, and up-to-date: The Eco-Movement database is refreshed every day.

When you want to show charging station information on a map or in an application, high quality data is crucial for the customer experience. Our real-time API is the easy solution to all your EV Charging Station related data needs. It is based on the industry standard OCPI protocol, and optionally we can add many additional enriching features.

Location attributes include coordinates, address, operator, power, connector type, opening times, access type (public / restricted / private), predicted occupancy, reliability score, and accepted payment methods. Tariff attributes include price per kWh, per hour charging and/or parking, flat fees, and subscription fees. Attributes are available for all countries in our database. The price of the data is dependent on the geographies chosen, the length of the subscription, and the intended use.

Check out our other Data Offerings available, and gain more valuable market insights on EV charging directly from the experts.

ALSO AVAILABLE We also offer EV Charging Station Location & Tariffs Data via a downloadable CSV, and offer a separate CSV report focused specifically on DC station hardware manufacturer and model information. The perfect inputs for your analysis, easily importable into e.g. Excel and Tableau.

ABOUT US

Eco-Movement's mission is providing the EV ecosystem with the best and most relevant Charging Station information. Based in Utrecht, the Netherlands, Eco-Movement is completely independent from other industry players. We are an active and trusted player in the EV ecosystem and the exclusive source for European Commission charging infrastructure data (EAFO).

This data set models the IEEE 14-bus system for studies on P2P electricity markets, including real data of consumption, solar and wind power from Australia. This data set is characterized by 30 minutes time-step over one year, i.e. from July 2012 to June 2013.

The transmission system comprises 14 buses and 20 lines, and its characteristics are based on [1]. The original number of generators was increased to 8 generators, i.e. 1 coal-based generator, 2 gas-based generators, 3 wind turbines and 2 PV plants. The data set uses the original number of 11 loads.

The bus 1 represents the upstream connection to the main grid, where the generator assumes an infinite power. The market price from the Australian Energy Market Operator is used in this generator. It is assumed the same period from July 2012 to June 2013 [4]. This data set supposes a tariff of 10$/MWh for using the main grid. The energy imported and exported in bus 1 has to account this extra cost. Thus, the exportation price is equal to the market price minus this grid tariff. On the other hand, the importation price is equal to the market price plus this grid tariff.

The wind production has been based on the data set from [2]. The time resolution has been converted from 5 minutes to 30 minutes. The authors would like to acknowledge that the data set in [2] was processed by Stefanos Delikaraoglou and Jethro Dowell. The solar production and load consumption are taken from [3]. The load consumption is split into fixed and flexible consumption per time-step. Since there is no access to the total capacity of the flexible consumption, we split the daily flexible consumption over each time-step. In this way, the maximum consumption is equal to the fixed consumption plus twice this flexible consumption per time-step. The minimum consumption is equal to the fixed consumption in each time-step.

The wind, solar and load data sets have been normalized, i.e. values relative to rated power. Then, these normalized sequences were multiplied by the capacity of each element. The data is intended for use in studies related to consumer-centric electricity markets, e.g.:

Validate new market designs or business models;

Assess the impact of new grid operation strategies;

Test the effect of strategic behavior by producers or consumers.

This dataset contains an annual summary of the car parking account from South Lakeland District Council.

Please note; figures published in April are interim figures and will be updated later in the year once the final accounts have been audited.

These include a breakdown of the income and expenditure on the parking account. SLDC’s car parking account only relates to the off-street car parks that it owns or manages; it does not collect for, or enforce any on-street parking.

Expenditure includes cost for the provision and maintenance of designated off street parking places by the local authority.

Surplus statement: There are strict rules established by Government that stipulate how surplus parking funds are spent, but there are differences between on and off-street parking Any surpluses from on-street parking and both on and off-street enforcement must be used in accordance with section 55 of the Road Traffic Regulation Act 1984. This means that any income remaining after enforcement costs must be used for transport. Income from off-street parking fees and charges can be for general use by the local authority.

The Council only operates off-street car parks and enforcement of them. This authority has to balance the parking needs of residents plus a much-increased transient population visiting the area. We are always looking at ways of improving use of our car parks, such as increasing capacity of underused car parks. There has been no overall increase in tariffs since 2011-12 and lower charges or new tariffs have been established with the aim of making best use of resources and reducing congestion. For example, we have introduced an ‘Early Bird’ tariff in Westmorland Shopping Centre car park saving £3.80 on the daily rate, and set a tariff of £1.20 for all-day parking in a car park in Grange-over-Sands, saving £4.50. Any surplus funds raised from off-street parking facilities after expenditure including car park maintenance and improvement, are used to offset the costs to the Council of providing services to the public, which would otherwise have to be met through Council Tax.

Further information about the council’s car parks including location, number of parking bays, number of disabled parking spaces and charges can be found in the Car Parks in South Lakeland dataset.

SA: Cost to Imports: USD per Container data was reported at 1,309.000 USD in 2014. This records an increase from the previous number of 1,229.000 USD for 2013. SA: Cost to Imports: USD per Container data is updated yearly, averaging 932.000 USD from Dec 2005 (Median) to 2014, with 10 observations. The data reached an all-time high of 1,309.000 USD in 2014 and a record low of 604.000 USD in 2006. SA: Cost to Imports: USD per Container data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Saudi Arabia – Table SA.World Bank.WDI: Company Statistics. Cost measures the fees levied on a 20-foot container in U.S. dollars. All the fees associated with completing the procedures to export or import the goods are included. These include costs for documents, administrative fees for customs clearance and technical control, customs broker fees, terminal handling charges and inland transport. The cost measure does not include tariffs or trade taxes. Only official costs are recorded.; ; World Bank, Doing Business project (http://www.doingbusiness.org/).; Unweighted average; Data are presented for the survey year instead of publication year.

Export Prices YoY in South Korea decreased to -2.40 percent in May from 0.40 percent in April of 2025. This dataset includes a chart with historical data for South Korea Export Prices YoY.